MLS-C01 AWS Certified Machine Learning - Specialty Questions and Answers

 The solution A and C are the most effective in solving the issue while keeping costs to a minimum. The solution A and C involve the following steps:

Configure the endpoint to use Amazon Elastic Inference (EI) accelerators. This will enable the company to reduce the cost and latency of running TensorFlow inference on SageMaker. Amazon EI provides GPU-powered acceleration for deep learning models without requiring the use of GPU instances. Amazon EI can attach to any SageMaker instance type and provide the right amount of acceleration based on the workload1.

Configure the endpoint to automatically scale with the Invocations Per Instance metric. This will enable the company to adjust the number of instances based on the demand and traffic patterns of the website. The Invocations Per Instance metric measures the average number of requests that each instance processes over a period of time. By using this metric, the company can scale out the endpoint when the load increases and scale in when the load decreases. This can improve the response time and availability of the product recommendation engine2.

The other options are not suitable because:

Option B: Creating a new endpoint configuration with two production variants will not solve the issue of increasing response time and errors. Production variants are used to split the traffic between different models or versions of the same model. They can be useful for testing, updating, or A/B testing models. However, they do not provide any scaling or acceleration benefits for the inference workload3.

Option D: Deploying a second instance pool to support a blue/green deployment of models will not solve the issue of increasing response time and errors. Blue/green deployment is a technique for updating models without downtime or disruption. It involves creating a new endpoint configuration with a different instance pool and model version, and then shifting the traffic from the old endpoint to the new endpoint gradually. However, this technique does not provide any scaling or acceleration benefits for the inference workload4.

Option E: Reconfiguring the endpoint to use burstable instances will not solve the issue of increasing response time and errors. Burstable instances are instances that provide a baseline level of CPU performance with the ability to burst above the baseline when needed. They can be useful for workloads that have moderate CPU utilization and occasional spikes. However, they are not suitable for workloads that have high and consistent CPU utilization, such as the product recommendation engine. Moreover, burstable instances may incur additional charges when they exceed their CPU credits5.

1: Amazon Elastic Inference

2: How to Scale Amazon SageMaker Endpoints

3: Deploying Models to Amazon SageMaker Hosting Services

4: Updating Models in Amazon SageMaker Hosting Services

5: Burstable Performance Instances

The task involves periodic inference (hourly batches of telemetry data) that can be processed in bulk, with the total processing time under 5 minutes. The most cost-effective solution for this batch prediction scenario is SageMaker Batch Transform, as it avoids the need for always-on endpoints.

“Use Batch Transform when you: … Don’t need a persistent endpoint and want to process batches of data at once. This is typically the most cost-efficient way to process large volumes of input data periodically.”

Since the use case doesn't require low latency or asynchronous real-time response, and processing takes only minutes, Batch Transform is the optimal fit.

Residuals are the differences between the actual and predicted values of the target variable in a regression model. A histogram of residuals is a graphical tool that can help evaluate the performance and assumptions of the model. Ideally, the histogram of residuals should have a zero-centered bell shape, which indicates that the residuals are normally distributed with a mean of zero and a constant variance. This means that the model has captured the true relationship between the input and output variables, and that the errors are random and unbiased. However, if the histogram of residuals does not have a zero-centered bell shape, as shown in the image, this means that the model might have prediction errors over a range of target values. This is because the residuals do not form a symmetrical and homogeneous distribution around zero, which implies that the model has some systematic bias or heteroscedasticity. This can affect the accuracy and validity of the model, and indicate that the model needs to be improved or modified.

Residual Analysis in Regression - Statistics By Jim

How to Check Residual Plots for Regression Analysis - dummies

Histogram of Residuals - Statistics How To

The XGBoost algorithm is a popular machine learning technique for classification problems. It is based on the idea of boosting, which is to combine many weak learners (decision trees) into a strong learner (ensemble model).

The XGBoost algorithm can handle imbalanced data by using the scale_pos_weight parameter, which controls the balance of positive and negative weights in the objective function. A typical value to consider is the ratio of negative cases to positive cases in the data. By increasing this parameter, the algorithm will pay more attention to the minority class (positive) and reduce the number of false negatives.

The XGBoost algorithm can also use different evaluation metrics to optimize the model performance. The default metric is error, which is the misclassification rate. However, this metric can be misleading for imbalanced data, as it does not account for the different costs of false positives and false negatives. A better metric to use is AUC, which is the area under the receiver operating characteristic (ROC) curve. The ROC curve plots the true positive rate against the false positive rate for different threshold values. The AUC measures how well the model can distinguish between the two classes, regardless of the threshold. By changing the eval_metric parameter to AUC, the algorithm will try to maximize the AUC score and reduce the number of false negatives.

Therefore, the combination of steps that should be taken to reduce the number of false negatives are to increase the scale_pos_weight parameter and change the eval_metric parameter to AUC.

XGBoost Parameters

XGBoost for Imbalanced Classification

 To use a custom algorithm in Amazon SageMaker, the Docker container image must have an executable file named train that acts as the ENTRYPOINT for the container. This file is responsible for running the training code and communicating with the Amazon SageMaker service. The train file must be in the PATH of the container and have execute permissions. The other options are not valid ways to package the Docker container for Amazon SageMaker. References:

Use Docker containers to build models - Amazon SageMaker

Create a container with your own algorithms and models - Amazon SageMaker

Option A is correct because SageMaker Pipelines is a service that enables you to create and manage automated workflows for your machine learning projects. You can use SageMaker Pipelines to orchestrate the steps of data extraction, model training, and model deployment in a repeatable and scalable way1.

Option B is correct because SageMaker Model Monitor is a service that monitors the quality of your models in production and alerts you when there are deviations in the model quality. You can use SageMaker Model Monitor to set an accuracy threshold for your model and configure a CloudWatch alarm that triggers when the threshold is exceeded. You can then connect the alarm to the workflow in SageMaker Pipelines to automatically initiate retraining and deployment of a new version of the model2.

Option C is incorrect because it is not the most operationally efficient way to maintain the model’s accuracy. Creating a daily SageMaker Processing job that reads the predictions from Amazon S3 and checks for changes in model prediction accuracy is a manual and time-consuming process. It also requires you to write custom code to perform the data analysis and send the email notification. Moreover, it does not automatically retrain and deploy the model when the accuracy drops.

Option D is incorrect because it is not the most operationally efficient way to maintain the model’s accuracy. Rerunning the steps in the Jupyter notebook that is hosted on SageMaker Studio notebooks to retrain the model and redeploy a new version of the model is a manual and error-prone process. It also requires you to monitor the model’s performance and initiate the retraining and deployment steps yourself. Moreover, it does not leverage the benefits of SageMaker Pipelines and SageMaker Model Monitor to automate and streamline the workflow.

Option E is incorrect because it is not the most operationally efficient way to maintain the model’s accuracy. Exporting the training and deployment code from the SageMaker Studio notebooks into a Python script and packaging the script into an Amazon ECS task that an AWS Lambda function can initiate is a complex and cumbersome process. It also requires you to manage the infrastructure and resources for the Amazon ECS task and the AWS Lambda function. Moreover, it does not leverage the benefits of SageMaker Pipelines and SageMaker Model Monitor to automate and streamline the workflow.

1: SageMaker Pipelines - Amazon SageMaker

2: Monitor data and model quality - Amazon SageMaker

To fix the incorrectly skewed data, the Data Scientist can apply two feature transformations: numerical value binning and logarithmic transformation. Numerical value binning is a technique that groups continuous values into discrete bins or categories. This can help reduce the skewness of the data by creating more balanced frequency distributions. Logarithmic transformation is a technique that applies the natural logarithm function to each value in the data. This can help reduce the right skewness of the data by compressing the large values and expanding the small values. Both of these transformations can make the data more suitable for machine learning algorithms that assume normality of the data. References:

Data Transformation - Amazon SageMaker

Transforming Skewed Data for Machine Learning

The company wants to perform near-real time defect detection on a time-series of 200 performance metrics, and store all the data for offline analysis. The best approach for this scenario is to use Amazon Kinesis Data Firehose for ingestion and Amazon Kinesis Data Analytics Random Cut Forest (RCF) to perform anomaly detection. Use Kinesis Data Firehose to store data in Amazon S3 for further analysis.

Amazon Kinesis Data Firehose is a service that can capture, transform, and deliver streaming data to destinations such as Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and Splunk. Kinesis Data Firehose can handle any amount and frequency of data, and automatically scale to match the throughput. Kinesis Data Firehose can also compress, encrypt, and batch the data before delivering it to the destination, reducing the storage cost and enhancing the security.

Amazon Kinesis Data Analytics is a service that can analyze streaming data in real time using SQL or Apache Flink applications. Kinesis Data Analytics can use built-in functions and algorithms to perform various analytics tasks, such as aggregations, joins, filters, windows, and anomaly detection. One of the built-in algorithms that Kinesis Data Analytics supports is Random Cut Forest (RCF), which is a supervised learning algorithm for forecasting scalar time series using recurrent neural networks. RCF can detect anomalies in streaming data by assigning an anomaly score to each data point, based on how distant it is from the rest of the data. RCF can handle multiple related time series, such as the performance metrics of the aircraft engine, and learn a global model that captures the common patterns and trends across the time series.

Therefore, the company can use the following architecture to build the near-real time defect detection solution:

Use Amazon Kinesis Data Firehose for ingestion: The company can use Kinesis Data Firehose to capture the streaming data from the aircraft engine testing, and deliver it to two destinations: Amazon S3 and Amazon Kinesis Data Analytics. The company can configure the Kinesis Data Firehose delivery stream to specify the source, the buffer size and interval, the compression and encryption options, the error handling and retry logic, and the destination details.

Use Amazon Kinesis Data Analytics Random Cut Forest (RCF) to perform anomaly detection: The company can use Kinesis Data Analytics to create a SQL application that can read the streaming data from the Kinesis Data Firehose delivery stream, and apply the RCF algorithm to detect anomalies. The company can use the RANDOM_CUT_FOREST or RANDOM_CUT_FOREST_WITH_EXPLANATION functions to compute the anomaly scores and attributions for each data point, and use the WHERE clause to filter out the normal data points. The company can also use the CURSOR function to specify the input stream, and the PUMP function to write the output stream to another destination, such as Amazon Kinesis Data Streams or AWS Lambda.

Use Kinesis Data Firehose to store data in Amazon S3 for further analysis: The company can use Kinesis Data Firehose to store the raw and processed data in Amazon S3 for offline analysis. The company can use the S3 destination of the Kinesis Data Firehose delivery stream to store the raw data, and use another Kinesis Data Firehose delivery stream to store the output of the Kinesis Data Analytics application. The company can also use AWS Glue or Amazon Athena to catalog, query, and analyze the data in Amazon S3.

What Is Amazon Kinesis Data Firehose?

What Is Amazon Kinesis Data Analytics for SQL Applications?

DeepAR Forecasting Algorithm - Amazon SageMaker

The best technique to improve the recall of the MLP for the target class of interest is to add class weights to the MLP’s loss function and then retrain. Class weights are a way of assigning different importance to each class in the dataset, such that the model will pay more attention to the classes with higher weights. This can help mitigate the class imbalance problem, where the model tends to favor the majority class and ignore the minority class. By increasing the weight of the target class of interest, the model will try to reduce the false negatives and increase the true positives, which will improve the recall metric. Adding class weights to the loss function is also a quick and easy solution, as it does not require gathering more data, changing the model architecture, or switching to a different algorithm.

AWS Machine Learning Specialty Exam Guide

AWS Machine Learning Training - Deep Learning with Amazon SageMaker

AWS Machine Learning Training - Class Imbalance and Weighted Loss Functions

 A convolutional neural network (CNN) is a type of machine learning algorithm that is suitable for image classification tasks. A CNN consists of multiple layers that can extract features from images and learn to recognize patterns and objects. A CNN can also use transfer learning to leverage pre-trained models that have been trained on large-scale image datasets, such as ImageNet, and fine-tune them for specific tasks, such as detecting the company’s retail brand. A CNN can achieve high accuracy and performance for image classification problems, as it can handle complex and diverse images and reduce the dimensionality and noise of the input data. A CNN can be implemented using various frameworks and libraries, such as TensorFlow, PyTorch, Keras, MXNet, etc12

The other options are not valid or relevant for the image classification task. Latent Dirichlet Allocation (LDA) is a type of machine learning algorithm that is suitable for topic modeling tasks. LDA can discover the hidden topics and their proportions in a collection of text documents, such as news articles, tweets, reviews, etc. LDA is not applicable for image data, as it requires textual input and output. LDA can be implemented using various frameworks and libraries, such as Gensim, Scikit-learn, Mallet, etc34

Recurrent neural network (RNN) is a type of machine learning algorithm that is suitable for sequential data tasks. RNN can process and generate data that has temporal or sequential dependencies, such as natural language, speech, audio, video, etc. RNN is not optimal for image data, as it does not capture the spatial features and relationships of the pixels. RNN can be implemented using various frameworks and libraries, such as TensorFlow, PyTorch, Keras, MXNet, etc.

K-means is a type of machine learning algorithm that is suitable for clustering tasks. K-means can partition a set of data points into a predefined number of clusters, based on the similarity and distance between the data points. K-means is not suitable for image classification tasks, as it does not learn to label the images or detect the objects of interest. K-means can be implemented using various frameworks and libraries, such as Scikit-learn, TensorFlow, PyTorch, etc.

The MAPE of the predictor could be improved by making the following changes to the CreatePredictor API call:

Set PerformAutoML to true. This will allow Amazon Forecast to automatically evaluate different algorithms and choose the one that minimizes the objective function, which is the mean of the weighted losses over the forecast types. By default, these are the p10, p50, and p90 quantile losses1. This option can help find a better algorithm than DeepAR+ for the given data.

Set PerformHPO to true. This will enable hyperparameter optimization (HPO), which is the process of finding the optimal values for the algorithm-specific parameters that affect the quality of the forecasts. HPO can improve the accuracy of the predictor by tuning the hyperparameters based on the training data2.

The other options are not likely to improve the MAPE of the predictor. Setting ForecastHorizon to 4 will reduce the number of time steps that the model predicts, which may not match the business requirement of predicting next month’s inventory. Setting ForecastFrequency to W for weekly will change the granularity of the forecasts, which may not be appropriate for the monthly data. Setting FeaturizationMethodName to filling will not have any effect, since there is no missing data in the dataset.

CreatePredictor - Amazon Forecast

HPOConfig - Amazon Forecast

The best solution is to configure Amazon EventBridge to run a predefined SageMaker pipeline to perform the transformations when a new data is detected in the S3 bucket. This solution requires the least development effort because it leverages the native integration between EventBridge and SageMaker Pipelines, which allows you to trigger a pipeline execution based on an event rule. EventBridge can monitor the S3 bucket for new data uploads and invoke the pipeline that contains the same transformations and feature engineering steps that were defined in SageMaker Data Wrangler. The pipeline can then ingest the transformed data into the online feature store for training and inference.

The other solutions are less optimal because they require more development effort and additional services. Using AWS Lambda or AWS Step Functions would require writing custom code to invoke the SageMaker pipeline and handle any errors or retries. Using Apache Airflow would require setting up and maintaining an Airflow server and DAGs, as well as integrating with the SageMaker API.

Amazon EventBridge and Amazon SageMaker Pipelines integration

Create a pipeline using a JSON specification

Ingest data into a feature group

The correct answer is D because it allows the company to use the existing SageMaker endpoint and leverage the built-in functionality of production variants for A/B testing. Production variants can be used to test ML models that have been trained using different training datasets, algorithms, and ML frameworks; test how they perform on different instance types; or a combination of all of the above1. By specifying the weight for each production variant in the endpoint configuration, the company can control how much traffic to send to each variant. By setting the TargetVariant header in the request, the company can invoke a specific variant directly for each request2. This enables the company to implement the required A/B testing setup without creating additional endpoints or load balancers.

1: Production variants - Amazon SageMaker

2: A/B Testing ML models in production using Amazon SageMaker | AWS Machine Learning Blog

The best solution to meet the requirements is to apply Synthetic Minority Oversampling Technique (SMOTE) to the training dataset before training. SMOTE is a technique that generates synthetic samples for the minority class by interpolating between existing samples. This can help balance the class distribution and provide more information to the model. SMOTE can improve the performance of the model on the minority class, which is the class of interest in churn prediction. SMOTE can be applied using the SageMaker Data Wrangler, which provides a built-in analysis for oversampling the minority class1.

The other options are not effective solutions for the problem. Applying anomaly detection to remove outliers from the training dataset before training may not improve the model’s accuracy, as outliers may not be the main cause of the false results. Moreover, removing outliers may reduce the diversity of the data and make the model less robust. Applying normalization to the features of the training dataset before training may improve the model’s convergence and stability, but it does not address the class imbalance issue. Normalization can also be applied using the SageMaker Data Wrangler, which provides a built-in transformation for scaling the features2. Applying undersampling to the training dataset before training may reduce the class imbalance, but it also discards potentially useful information from the majority class. Undersampling can also result in underfitting and high bias for the model.

The goal of classification is to determine to which class or category a data point (customer in our case) belongs to. For classification problems, data scientists would use historical data with predefined target variables AKA labels (churner/non-churner) – answers that need to be predicted – to train an algorithm. With classification, businesses can answer the following questions:

Will this customer churn or not?

Will a customer renew their subscription?

Will a user downgrade a pricing plan?

Are there any signs of unusual customer behavior?

 AWS KMS is a service that provides encryption and key management for data stored in AWS services and applications. AWS KMS can generate and manage encryption keys that are used to encrypt and decrypt data at rest and in transit. AWS KMS can also integrate with other AWS services, such as Amazon S3 and Amazon SageMaker, to enable encryption of data using the keys stored in AWS KMS. Amazon S3 is a service that provides object storage for data in the cloud. Amazon S3 can use AWS KMS to encrypt data at rest using server-side encryption with AWS KMS-managed keys (SSE-KMS). Amazon SageMaker is a service that provides a platform for building, training, and deploying machine learning models. Amazon SageMaker can use AWS KMS to encrypt data at rest on the SageMaker instances and volumes, as well as data in transit between SageMaker and other AWS services. AWS Glue is a service that provides a serverless data integration platform for data preparation and transformation. AWS Glue can use AWS KMS to encrypt data at rest on the Glue Data Catalog and Glue ETL jobs. AWS Glue can also use built-in or custom classifiers to identify and redact sensitive data, such as credit card numbers, from the customer data1234

The other options are not valid or secure ways to encrypt the data and protect the credit card information. Using a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC is not a good practice, as custom encryption algorithms are not recommended for security and may have flaws or vulnerabilities. Using the SageMaker DeepAR algorithm to randomize the credit card numbers is not a good practice, as DeepAR is a forecasting algorithm that is not designed for data anonymization or encryption. Using an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers is not a good practice, as IAM policies are not meant for data encryption, but for access control and authorization. Amazon Kinesis is a service that provides real-time data streaming and processing, but it does not have the capability to automatically discard or insert data values. Using an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC is not a good practice, as launch configurations are not meant for data encryption, but for specifying the instance type, security group, and user data for the SageMaker instance. Using the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers is not a good practice, as PCA is a dimensionality reduction algorithm that is not designed for data anonymization or encryption.

The problem of poor performance on the test data is a sign of overfitting, which means the model has learned the training data too well and failed to generalize to new and unseen data. To correct this, the Machine Learning Specialist should consider using methods that reduce the complexity of the model and increase its ability to generalize. Some of these methods are:

Increase regularization: Regularization is a technique that adds a penalty term to the loss function of the model, which reduces the magnitude of the model weights and prevents overfitting. There are different types of regularization, such as L1, L2, and elastic net, that apply different penalties to the weights1.

Increase dropout: Dropout is a technique that randomly drops out some units or connections in the neural network during training, which reduces the co-dependency of the units and prevents overfitting. Dropout can be applied to different layers of the network, and the dropout rate can be tuned to control the amount of dropout2.

Decrease feature combinations: Feature combinations are the interactions between different input features that can be used to create new features for the model. However, too many feature combinations can increase the complexity of the model and cause overfitting. Therefore, the Specialist should decrease the number of feature combinations and select only the most relevant and informative ones for the model3.

1: Regularization for Deep Learning - Amazon SageMaker

2: Dropout - Amazon SageMaker

3: Feature Engineering - Amazon SageMaker

 The best combination of AWS services to meet the requirements of data discovery, enrichment, transformation, querying, analysis, and reporting with the least coding and infrastructure management is AWS Glue, Amazon Athena, and Amazon QuickSight. These services are:

AWS Glue for data discovery, enrichment, and transformation. AWS Glue is a serverless data integration service that automatically crawls, catalogs, and prepares data from various sources and formats. It also provides a visual interface called AWS Glue DataBrew that allows users to apply over 250 transformations to clean, normalize, and enrich data without writing code1

Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL. Amazon Athena is a serverless interactive query service that allows users to analyze data in Amazon S3 using standard SQL. It supports a variety of data formats, such as CSV, JSON, ORC, Parquet, and Avro. It also integrates with AWS Glue Data Catalog to provide a unified view of the data sources and schemas2

Amazon QuickSight for reporting and getting insights. Amazon QuickSight is a serverless business intelligence service that allows users to create and share interactive dashboards and reports. It also provides ML-powered features, such as anomaly detection, forecasting, and natural language queries, to help users discover hidden insights from their data3

The other options are not suitable because they either require more coding effort, more infrastructure management, or do not support the desired use cases. For example:

Option A uses Amazon EMR for data discovery, enrichment, and transformation. Amazon EMR is a managed cluster platform that runs Apache Spark, Apache Hive, and other open-source frameworks for big data processing. It requires users to write code in languages such as Python, Scala, or SQL to perform data integration tasks. It also requires users to provision, configure, and scale the clusters according to their needs4

Option B uses Amazon Kinesis Data Analytics for data ingestion. Amazon Kinesis Data Analytics is a service that allows users to process streaming data in real time using SQL or Apache Flink. It is not suitable for data discovery, enrichment, and transformation, which are typically batch-oriented tasks. It also requires users to write code to define the data processing logic and the output destination5

Option D uses AWS Data Pipeline for data transfer and AWS Step Functions for orchestrating AWS Lambda jobs for data discovery, enrichment, and transformation. AWS Data Pipeline is a service that helps users move data between AWS services and on-premises data sources. AWS Step Functions is a service that helps users coordinate multiple AWS services into workflows. AWS Lambda is a service that lets users run code without provisioning or managing servers. These services require users to write code to define the data sources, destinations, transformations, and workflows. They also require users to manage the scalability, performance, and reliability of the data pipelines.

1: AWS Glue - Data Integration Service - Amazon Web Services

2: Amazon Athena – Interactive SQL Query Service - AWS

3: Amazon QuickSight - Business Intelligence Service - AWS

4: Amazon EMR - Amazon Web Services

5: Amazon Kinesis Data Analytics - Amazon Web Services

AWS Data Pipeline - Amazon Web Services

AWS Step Functions - Amazon Web Services

AWS Lambda - Amazon Web Services

The best way to meet the requirements is to use a principal component analysis (PCA) model, which is a technique that reduces the dimensionality of the dataset by transforming the original variables into a smaller set of new variables, called principal components, that capture most of the variance and information in the data1. This technique has the following advantages:

It can increase the accuracy of the model by removing noise, redundancy, and multicollinearity from the data, and by enhancing the interpretability and generalization of the model23.

It can decrease the processing time of the model by reducing the number of features and the computational complexity of the model, and by improving the convergence and stability of the model45.

It is suitable for numeric variables, as it relies on the covariance or correlation matrix of the data, and it can handle a large quantity of variables, as it can extract the most relevant ones16.

The other options are not effective or appropriate, because they have the following drawbacks:

A: Creating new features and interaction variables can increase the accuracy of the model by capturing more complex and nonlinear relationships in the data, but it can also increase the processing time of the model by adding more features and increasing the computational complexity of the model7. Moreover, it can introduce more noise, redundancy, and multicollinearity in the data, which can degrade the performance and interpretability of the model8.

C: Applying normalization on the feature set can increase the accuracy of the model by scaling the features to a common range and avoiding the dominance of some features over others, but it can also decrease the processing time of the model by reducing the numerical instability and improving the convergence of the model . However, normalization alone is not enough to address the high dimensionality and high latency issues of the dataset, as it does not reduce the number of features or the variance in the data.

D: Using a multiple correspondence analysis (MCA) model is not suitable for numeric variables, as it is a technique that reduces the dimensionality of the dataset by transforming the original categorical variables into a smaller set of new variables, called factors, that capture most of the inertia and information in the data. MCA is similar to PCA, but it is designed for nominal or ordinal variables, not for continuous or interval variables.

1: Principal Component Analysis - Amazon SageMaker

2: How to Use PCA for Data Visualization and Improved Performance in Machine Learning | by Pratik Shukla | Towards Data Science

3: Principal Component Analysis (PCA) for Feature Selection and some of its Pitfalls | by Nagesh Singh Chauhan | Towards Data Science

4: How to Reduce Dimensionality with PCA and Train a Support Vector Machine in Python | by James Briggs | Towards Data Science

5: Dimensionality Reduction and Its Applications | by Aniruddha Bhandari | Towards Data Science

6: Principal Component Analysis (PCA) in Python | by Susan Li | Towards Data Science

7: Feature Engineering for Machine Learning | by Dipanjan (DJ) Sarkar | Towards Data Science

8: Feature Engineering — How to Engineer Features and How to Get Good at It | by Parul Pandey | Towards Data Science

[Feature Scaling for Machine Learning: Understanding the Difference Between Normalization vs. Standardization | by Benjamin Obi Tayo Ph.D. | Towards Data Science]

[Why, How and When to Scale your Features | by George Seif | Towards Data Science]

[Normalization vs Dimensionality Reduction | by Saurabh Annadate | Towards Data Science]

[Multiple Correspondence Analysis - Amazon SageMaker]

[Multiple Correspondence Analysis (MCA) | by Raul Eulogio | Towards Data Science]

The combination of actions that will meet the requirements with the least operational overhead is to use SageMaker Clarify to automatically detect data bias and to configure SageMaker Data Wrangler to generate a bias report. SageMaker Clarify is a feature of Amazon SageMaker that provides machine learning (ML) developers with tools to gain greater insights into their ML training data and models. SageMaker Clarify can detect potential bias during data preparation, after model training, and in your deployed model. For instance, you can check for bias related to age in your dataset or in your trained model and receive a detailed report that quantifies different types of potential bias1. SageMaker Data Wrangler is another feature of Amazon SageMaker that enables you to prepare data for machine learning (ML) quickly and easily. You can use SageMaker Data Wrangler to identify potential bias during data preparation without having to write your own code. You specify input features, such as gender or age, and SageMaker Data Wrangler runs an analysis job to detect potential bias in those features. SageMaker Data Wrangler then provides a visual report with a description of the metrics and measurements of potential bias so that you can identify steps to remediate the bias2. The other actions either require more customization (such as using SageMaker Model Monitor or SageMaker Experiments) or do not meet the requirement of detecting data bias (such as using SageMaker Ground Truth). References:

1: Bias Detection and Model Explainability – Amazon Web Services

2: Amazon SageMaker Data Wrangler – Amazon Web Services

CNN-QR is a proprietary machine learning algorithm for forecasting time series using causal convolutional neural networks (CNNs). CNN-QR works best with large datasets containing hundreds of time series. It accepts item metadata, and is the only Forecast algorithm that accepts related time series data without future values. In this case, the power company has historical power consumption data for the last 10 years, which is a large dataset with multiple time series. The data also includes related data such as weather, number of individuals on the property, and public holidays, which can be used as item metadata or related time series data. Therefore, CNN-QR is the most suitable algorithm for this scenario. References: Amazon Forecast Algorithms, Amazon Forecast CNN-QR

 Amazon SageMaker is a service that allows users to build, train, and deploy ML models on AWS. Amazon SageMaker endpoints are scalable and secure web services that can be used to perform real-time inference on ML models. An endpoint configuration defines the models that are deployed and the resources that are used by the endpoint. An endpoint configuration can have multiple production variants, each representing a different version or variant of a model. Users can specify the portion of the inferences served by each production variant using the initialVariantWeight parameter. Users can also programmatically update the endpoint configuration to change the portion of the inferences served by each production variant using the UpdateEndpointWeightsAndCapacities API. Therefore, option B is the best solution to satisfy the requirements with minimal effort.

Option A is incorrect because creating multiple endpoints for each model would incur more cost and complexity than using a single endpoint with multiple production variants. Moreover, controlling the invocation of different models at the application layer would require more custom logic and coordination than using the UpdateEndpointWeightsAndCapacities API. Option C is incorrect because Amazon SageMaker Neo is a service that allows users to optimize ML models for different hardware platforms, such as edge devices. It is not relevant to the problem of running multiple versions of a model in parallel for long periods of time. Option D is incorrect because Amazon SageMaker batch transform is a service that allows users to perform asynchronous inference on large datasets. It is not suitable for the problem of performing real-time inference on streaming data from device users.

Deploying models to Amazon SageMaker hosting services - Amazon SageMaker

Update an Amazon SageMaker endpoint to accommodate new models - Amazon SageMaker

UpdateEndpointWeightsAndCapacities - Amazon SageMaker

Amazon Personalize is a fully managed machine learning service that makes it easy for developers to create personalized user experiences at scale. It uses the same recommender system technology that Amazon uses to create its own personalized recommendations. Amazon Personalize provides several pre-built recipes that can be used to train models for different use cases. The USER_PERSONALIZATION recipe is designed to provide personalized recommendations for existing users based on their past interactions with items. The PERSONALIZED_RANKING recipe is designed to re-rank a list of items for a user based on their preferences. The USER_PERSONALIZATION recipe is more suitable for this use case because it can generate recommendations for each user without requiring a list of candidate items. To filter out the items that the user previously purchased, a real-time filter can be created and applied to the campaign. A real-time filter is a dynamic filter that uses the latest interaction data to exclude items from the recommendations. By using Amazon Personalize, the development effort is minimized because it handles the data processing, model training, and deployment automatically. The web application can use the GetRecommendations API operation to get the real-time recommendations from the campaign. References:

Amazon Personalize

What is Amazon Personalize?

USER_PERSONALIZATION recipe

PERSONALIZED_RANKING recipe

Filtering recommendations

GetRecommendations API operation

Option C is correct because augmenting the images in the dataset can help the model learn more features and generalize better to new products. Image augmentation is a common technique to increase the diversity and size of the training data.

Option E is correct because Amazon Rekognition Custom Labels can train a custom model to detect specific objects and scenes that are relevant to the business use case. It can also leverage the existing models from Amazon Rekognition that are trained on tens of millions of images across many categories.

Option F is correct because class imbalance can affect the performance and accuracy of the model, as it can cause the model to be biased towards the majority class and ignore the minority class. Applying oversampling or undersampling can help balance the classes and improve the model’s ability to learn from the data.

Option A is incorrect because the semantic segmentation algorithm is used to assign a label to every pixel in an image, not to classify the whole image into a category. Semantic segmentation is useful for applications such as autonomous driving, medical imaging, and satellite imagery analysis.

Option B is incorrect because the DetectLabels API is a general-purpose image analysis service that can detect objects, scenes, and concepts in an image, but it cannot be customized to the specific product lines of the ecommerce company. The DetectLabels API is based on the pre-trained models from Amazon Rekognition, which may not cover all the categories that the company needs.

Option D is incorrect because normalizing the pixels and scaling the images are preprocessing steps that should be done before training the model, not after. These steps can help improve the model’s convergence and performance, but they are not sufficient to increase the accuracy of the model on new products.

 Image Augmentation - Amazon SageMaker

 Amazon Rekognition Custom Labels Features

[Handling Imbalanced Datasets in Machine Learning]

[Semantic Segmentation - Amazon SageMaker]

[DetectLabels - Amazon Rekognition]

[Image Classification - MXNet - Amazon SageMaker]

[https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28]

[https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html]

[https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html]

[https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html]

[https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28]

[https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html]

[https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html]

[https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html]

[https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28]

[https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html]

[https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html]

[https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html]

SageMaker’s TrainingJobEarlyStoppingType set to AUTO stops training jobs early when they no longer improve the objective metric, significantly reducing tuning job durations.

From AWS documentation:

“You can use early stopping to terminate training jobs that are unlikely to result in a better objective metric than previously completed jobs. This can reduce tuning costs and duration.”

— AWS SageMaker Automatic Model Tuning documentation

The solution C is the most likely to improve the data ingestion rate into Amazon S3 because it increases the number of shards for the data stream. The number of shards determines the throughput capacity of the data stream, which affects the rate of data ingestion. Each shard can support up to 1 MB per second of data input and 2 MB per second of data output. By increasing the number of shards, the company can increase the data ingestion rate proportionally. The company can use the UpdateShardCount API operation to modify the number of shards in the data stream1.

The other options are not likely to improve the data ingestion rate into Amazon S3 because:

Option A: Increasing the number of S3 prefixes for the delivery stream to write to will not affect the data ingestion rate, as it only changes the way the data is organized in the S3 bucket. The number of S3 prefixes can help to optimize the performance of downstream applications that read the data from S3, but it does not impact the performance of Kinesis Data Firehose2.

Option B: Decreasing the retention period for the data stream will not affect the data ingestion rate, as it only changes the amount of time the data is stored in the data stream. The retention period can help to manage the data availability and durability, but it does not impact the throughput capacity of the data stream3.

Option D: Adding more consumers using the Kinesis Client Library (KCL) will not affect the data ingestion rate, as it only changes the way the data is processed by downstream applications. The consumers can help to scale the data processing and handle failures, but they do not impact the data ingestion into S3 by Kinesis Data Firehose4.

1: Resharding - Amazon Kinesis Data Streams

2: Amazon S3 Prefixes - Amazon Kinesis Data Firehose

3: Data Retention - Amazon Kinesis Data Streams

4: Developing Consumers Using the Kinesis Client Library - Amazon Kinesis Data Streams

 The simplest and fastest way to use the EFS dataset for SageMaker training is to run a SageMaker training job with an EFS file system as the data source. This option does not require any data copying or additional integration steps. SageMaker supports EFS as a data source for training jobs, and it can mount the EFS file system to the training container using the FileSystemConfig parameter. This way, the training script can access the data files as if they were on the local disk of the training instance. References:

Access Training Data - Amazon SageMaker

Mount an EFS file system to an Amazon SageMaker notebook (with lifecycle configurations) | AWS Machine Learning Blog

B. Apply principal component analysis (PCA): PCA is a technique that reduces the dimensionality of a dataset by transforming the original features into a smaller set of new features that capture most of the variance in the data. PCA can help address the issue of multicollinearity, which occurs when some features are highly correlated with each other and can cause problems for some machine learning algorithms. By applying PCA, the data engineer can reduce the number of features and remove the redundancy in the data.

C. Remove a portion of highly correlated features from the dataset: Another way to deal with multicollinearity is to manually remove some of the features that are highly correlated with each other. This can help simplify the model and avoid overfitting. The data engineer can use the correlation matrix to identify the features that have a high correlation coefficient (e.g., above 0.8 or below -0.8) and remove one of them from the dataset. References: =

Principal Component Analysis: This is a document from AWS that explains what PCA is, how it works, and how to use it with Amazon SageMaker.

Multicollinearity: This is a document from AWS that describes what multicollinearity is, how to detect it, and how to deal with it.

Amazon Kinesis Data Analytics is a service that allows users to analyze streaming data in real time using SQL queries. Amazon Random Cut Forest (RCF) is a SQL extension that enables anomaly detection on streaming data. RCF is an unsupervised machine learning algorithm that assigns an anomaly score to each data point based on how different it is from the rest of the data. A sliding window is a type of window that moves along with the data stream, so that the anomaly detection model can adapt to changing patterns over time. A tumbling window is a type of window that has a fixed size and does not overlap with other windows, so that the anomaly detection model is based on a fixed period of time. Therefore, option D is the best approach to meet the requirements of the question, as it uses RCF to calculate anomaly scores for each web traffic entry and uses a sliding window to adapt to changing web patterns over time.

Option A is incorrect because Amazon SageMaker Random Cut Forest (RCF) is a built-in model that can be used to train and deploy anomaly detection models on batch or streaming data, but it requires more steps and resources than using the RCF SQL extension in Amazon Kinesis Data Analytics. Option B is incorrect because Amazon SageMaker XGBoost is a built-in model that can be used for supervised learning tasks such as classification and regression, but not for unsupervised learning tasks such as anomaly detection. Option C is incorrect because k-Nearest Neighbors (kNN) is a SQL extension that can be used for classification and regression tasks on streaming data, but not for anomaly detection. Moreover, using a tumbling window would not allow the anomaly detection model to adapt to changing web patterns over time.

Using CloudWatch anomaly detection

Anomaly Detection With CloudWatch

Performing Real-time Anomaly Detection using AWS

What Is AWS Anomaly Detection? (And Is There A Better Option?)

The data scientist should use the area under the precision-recall curve and the true positive rate to optimize the model. These metrics are suitable for imbalanced classification problems, such as credit card fraud detection, where the positive class (fraudulent transactions) is much rarer than the negative class (non-fraudulent transactions).

The area under the precision-recall curve (AUPRC) is a measure of how well the model can identify the positive class among all the predicted positives. Precision is the fraction of predicted positives that are actually positive, and recall is the fraction of actual positives that are correctly predicted. A higher AUPRC means that the model can achieve a higher precision with a higher recall, which is desirable for fraud detection.

The true positive rate (TPR) is another name for recall. It is also known as sensitivity or hit rate. It measures the proportion of actual positives that are correctly identified by the model. A higher TPR means that the model can capture more positives, which is the company’s goal.

Metrics for Imbalanced Classification in Python - Machine Learning Mastery

Precision-Recall - scikit-learn

The SageMaker Spark library is a library that enables Apache Spark applications to integrate with Amazon SageMaker for training and hosting machine learning models. The library provides several features, such as:

Estimators: Classes that allow Spark users to train Amazon SageMaker models and host them on Amazon SageMaker endpoints using the Spark MLlib Pipelines API. The library supports various built-in algorithms, such as linear learner, XGBoost, K-means, etc., as well as custom algorithms using Docker containers.

Model classes: Classes that wrap Amazon SageMaker models in a Spark MLlib Model abstraction. This allows Spark users to use Amazon SageMaker endpoints for inference within Spark applications.

Data sources: Classes that allow Spark users to read data from Amazon S3 using the Spark Data Sources API. The library supports various data formats, such as CSV, LibSVM, RecordIO, etc.

To integrate the Spark application with SageMaker, the Machine Learning Specialist should do the following:

Install the SageMaker Spark library in the Spark environment. This can be done by using Maven, pip, or downloading the JAR file from GitHub.

Use the appropriate estimator from the SageMaker Spark Library to train a model. For example, to train a linear learner model, the Specialist can use the following code:

Use the sageMakerModel. transform method to get inferences from the model hosted in SageMaker. For example, to get predictions for a test DataFrame, the Specialist can use the following code:

[SageMaker Spark]: A documentation page that introduces the SageMaker Spark library and its features.

[SageMaker Spark GitHub Repository]: A GitHub repository that contains the source code, examples, and installation instructions for the SageMaker Spark library.

In this scenario, Kinesis Data Streams efficiently ingests real-time event data, while Amazon Managed Service for Apache Flink (formerly Amazon Kinesis Data Analytics) is ideal for transforming and analyzing data in a continuous stream. Apache Flink allows processing of time-based windows, such as the 10-minute sliding window required here, with low operational overhead.

This combination provides an effective solution for low-latency data processing and transformation, meeting the requirements for preparing data for inference with minimal setup and serverless scalability.

 The engineer should create a private workforce and manifest file, and then create a labeling job by using the built-in bounding box task type in Amazon SageMaker Ground Truth. This will allow the engineer to build the labeling pipeline with the least effort.

A private workforce is a group of workers that you manage and who have access to your labeling tasks. You can use a private workforce to label sensitive data that requires confidentiality, such as medical images. You can create a private workforce by using Amazon Cognito and inviting workers by email. You can also use AWS Single Sign-On or your own authentication system to manage your private workforce.

A manifest file is a JSON file that lists the Amazon S3 locations of your input data. You can use a manifest file to specify the data objects that you want to label in your labeling job. You can create a manifest file by using the AWS CLI, the AWS SDK, or the Amazon SageMaker console.

A labeling job is a process that sends your input data to workers for labeling. You can use the Amazon SageMaker console to create a labeling job and choose from several built-in task types, such as image classification, text classification, semantic segmentation, and bounding box. A bounding box task type allows workers to draw boxes around objects in an image and assign labels to them. This is suitable for object detection tasks, such as identifying areas of concern on CT scans.

Create and Manage Workforces - Amazon SageMaker

Use Input and Output Data - Amazon SageMaker

Create a Labeling Job - Amazon SageMaker

Bounding Box Task Type - Amazon SageMaker

 To configure the notebook instance placement to meet the requirements, the Data Science team should associate the Amazon SageMaker notebook with a private subnet in a VPC. A VPC is a virtual network that is logically isolated from other networks in AWS. A private subnet is a subnet that has no internet gateway attached to it, and therefore cannot communicate with the internet. By placing the notebook instance in a private subnet, the team can ensure that it stays within a secured VPC with no internet access.

However, to access data stored in Amazon S3 buckets and other AWS services, the team needs to ensure that the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it. A VPC endpoint is a gateway that enables private connections between the VPC and supported AWS services. A VPC endpoint does not require an internet gateway, a NAT device, or a VPN connection, and ensures that the traffic between the VPC and the AWS service does not leave the AWS network. By using VPC endpoints, the team can access Amazon S3 and Amazon SageMaker from the notebook instance without compromising data privacy or security.

What Is Amazon VPC? - Amazon Virtual Private Cloud

Subnet Routing - Amazon Virtual Private Cloud

VPC Endpoints - Amazon Virtual Private Cloud

Linear regression is a machine learning approach that can be used to solve this problem. Linear regression is a supervised learning technique that can model the relationship between one or more input variables (features) and an output variable (target). In this case, the input variables could be the historical sales data of the part, such as the quarter, the demand, the price, the inventory, etc. The output variable could be the number of units to be produced for the part. Linear regression can learn the coefficients (weights) of the input variables that best fit the output variable, and then use them to make predictions for new data. Linear regression is suitable for problems that involve continuous and numeric output variables, such as predicting house prices, stock prices, or sales volumes. References:

AWS Machine Learning Specialty Exam Guide

Linear Regression

 The best option is to deploy the Lambda function and the ML models onto the AWS IoT Greengrass core that is running on the industrial PCs that are installed on each machine. This way, the inference can be performed locally on the edge devices, without the need to upload the images to Amazon S3 and invoke the SageMaker endpoint. This will reduce the latency and the network bandwidth consumption. The long-running Lambda function can be extended to invoke the Lambda function with the captured images and run the inference on the edge component that forwards the results directly to the web service. This will also simplify the architecture and eliminate the dependency on the internet connection.

Option A is not cost-effective, as it requires setting up a 10 Gbps AWS Direct Connect connection and increasing the size and number of instances for the SageMaker endpoint. This will increase the operational costs and complexity.

Option B is not optimal, as it still requires uploading the images to Amazon S3 and invoking the SageMaker endpoint. Compressing and decompressing the images will add additional processing overhead and latency.

Option C is not sufficient, as it still requires uploading the images to Amazon S3 and invoking the SageMaker endpoint. Auto scaling for SageMaker will help to handle the increased workload, but it will not reduce the latency or the network bandwidth consumption. Setting up an AWS Direct Connect connection will improve the network performance, but it will also increase the operational costs and complexity. References:

AWS IoT Greengrass

Deploying Machine Learning Models to Edge Devices

AWS Certified Machine Learning - Specialty Exam Guide

 The best approach to maximize transcription accuracy during the development phase is to create a custom vocabulary file containing each product name with phonetic pronunciations, and use it with Amazon Transcribe to perform the ASR customization. A custom vocabulary is a list of words and phrases that are likely to appear in your audio input, along with optional information about how to pronounce them. By using a custom vocabulary, you can improve the transcription accuracy of domain-specific terms, such as product names, that may not be recognized by the general vocabulary of Amazon Transcribe. You can also analyze the transcripts and manually update the custom vocabulary file to include updated or additional entries for those names that are not being correctly identified.

The other options are not as effective as option C for the following reasons:

Option A is not suitable because Amazon Lex is a service for building conversational interfaces, not for transcribing voicemail messages. Amazon Lex also has a limit of 100 slots per bot, which is not enough to accommodate the 200 unique product names required by the company.

Option B is not optimal because it relies on the word confidence scores in the transcript, which may not be accurate enough to identify all the mis-transcribed product names. Moreover, automatically creating or updating a custom vocabulary file may introduce errors or inconsistencies in the pronunciation or display of the words.

Option D is not feasible because it requires a large amount of training data to build a custom language model. The company only has 4,000 words of Amazon SageMaker Ground Truth voicemail transcripts, which is not enough to train a robust and reliable custom language model. Additionally, creating and updating a custom language model is a time-consuming and resource-intensive process, which may not be suitable for the development phase where frequent changes are expected.

Amazon Transcribe – Custom Vocabulary

Amazon Transcribe – Custom Language Models

[Amazon Lex – Limits]

Amazon SageMaker Data Wrangler is a tool that helps data scientists and ML developers to prepare data for ML. One of the features of Data Wrangler is the anomaly detection visualization, which uses an unsupervised ML algorithm to identify outliers in the dataset based on statistical properties. The ML specialist can use this feature to quickly explore the sensor data and find any anomalous values that may affect the model performance. The ML specialist can then add a transformation to a Data Wrangler data flow to remove the outliers from the dataset. The data flow can be exported as a script or a pipeline to automate the data preparation process. This option requires the least operational overhead compared to the other options.

Amazon SageMaker Data Wrangler - Amazon Web Services (AWS)

Anomaly Detection Visualization - Amazon SageMaker

Transform Data - Amazon SageMaker

 Amazon Kinesis Data Analytics is a service that can analyze streaming data in real time using SQL or Apache Flink applications. It can also use machine learning algorithms, such as Random Cut Forest (RCF), to perform anomaly detection on streaming data. By inserting a Kinesis Data Analytics stream downstream of the Kinesis Data Firehose stream, the retail chain can transform the raw record attributes into simple transformed values using SQL queries. This can be done without changing the existing data ingestion process or deploying additional resources. The transformed records can then be outputted to another Kinesis Data Firehose stream that delivers them to Amazon S3 for training the machine learning model. This approach will require the least amount of development effort, as it leverages the existing Kinesis Data Firehose stream and the built-in SQL capabilities of Kinesis Data Analytics.

Amazon Kinesis Data Analytics - Amazon Web Services

Anomaly Detection with Amazon Kinesis Data Analytics - Amazon Web Services

Amazon Kinesis Data Firehose - Amazon Web Services

Amazon S3 - Amazon Web Services

The true class frequency for Romance is the percentage of movies that are actually Romance out of all the movies. This can be calculated by dividing the sum of the true values for Romance by the total number of movies. The predicted class frequency for Adventure is the percentage of movies that are predicted to be Adventure out of all the movies. This can be calculated by dividing the sum of the predicted values for Adventure by the total number of movies. Based on the confusion matrix, the true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is 13.12%. References: Confusion Matrix, Classification Metrics

A convolutional neural network (CNN) is a type of deep learning model that can learn to extract features from images and perform tasks such as classification, segmentation, and detection1. ResNet is a popular CNN architecture that uses residual connections to overcome the problem of vanishing gradients and enable very deep networks2. For the task of predicting product quality based on sensor data, a CNN and ResNet approach can leverage the spatial structure of the data and learn complex patterns that distinguish different quality levels.

Convolutional Neural Networks (CNNs / ConvNets)

PyTorch ResNet: The Basics and a Quick Tutorial

One-hot encoding is a technique that can be used to convert a categorical variable, such as the Day-Of_Week column, to binary values. One-hot encoding creates a new binary column for each unique value in the original column, and assigns a value of 1 to the column that corresponds to the value in the original column, and 0 to the rest. For example, if the original column has values Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, and Sunday, one-hot encoding will create seven new columns, each representing one day of the week. If the value in the original column is Tuesday, then the column for Tuesday will have a value of 1, and the other columns will have a value of 0. One-hot encoding can help improve the performance of machine learning models, as it eliminates the ordinal relationship between the values and creates a more informative and sparse representation of the data.

One-Hot Encoding - Amazon SageMaker

One-Hot Encoding: A Simple Guide for Beginners | by Jana Schmidt …

One-Hot Encoding in Machine Learning | by Nishant Malik | Towards …

 The problem of detecting specific types of weeds and their location within the field is an example of object detection, which is a type of machine learning model that identifies and localizes objects in an image. Amazon SageMaker provides a built-in object detection algorithm that uses a single-shot multibox detector (SSD) to perform real-time inference on streaming images. The SSD algorithm can handle multiple objects of varying sizes and scales in an image, and generate bounding boxes and scores for each object category. Therefore, option C is the best approach to obtain accurate predictions.

Option A is incorrect because image classification is a type of machine learning model that assigns a label to an image based on predefined categories. Image classification is not suitable for localizing objects within an image, as it does not provide bounding boxes or scores for each object. Option B is incorrect because Apache Parquet is a columnar storage format that is optimized for analytical queries. Apache Parquet is not suitable for storing images, as it does not preserve the spatial information of the pixels. Option D is incorrect because it combines the wrong format (Apache Parquet) and the wrong algorithm (image classification) for the given problem, as explained in options A and B.

Object Detection algorithm now available in Amazon SageMaker

Image classification and object detection using Amazon Rekognition Custom Labels and Amazon SageMaker JumpStart

Object Detection with Amazon SageMaker - W3Schools

aws-samples/amazon-sagemaker-tensorflow-object-detection-api

The best approaches to decrease the training time of the model are C and D, because they can improve the computational efficiency and parallelization of the training process. These approaches have the following benefits:

C: Replacing CPU-based EC2 instances with GPU-based EC2 instances can speed up the training of the DeepAR algorithm, as it can leverage the parallel processing power of GPUs to perform matrix operations and gradient computations faster than CPUs12. The DeepAR algorithm supports GPU-based EC2 instances such as ml.p2 and ml.p33.

D: Using multiple training instances can also reduce the training time of the DeepAR algorithm, as it can distribute the workload across multiple nodes and perform data parallelism4. The DeepAR algorithm supports distributed training with multiple CPU-based or GPU-based EC2 instances3.

The other options are not effective or relevant, because they have the following drawbacks:

A: Replacing On-Demand Instances with Spot Instances can reduce the cost of the training, but not necessarily the time, as Spot Instances are subject to interruption and availability5. Moreover, the DeepAR algorithm does not support checkpointing, which means that the training cannot resume from the last saved state if the Spot Instance is terminated3.

B: Configuring model auto scaling dynamically to adjust the number of instances automatically is not applicable, as this feature is only available for inference endpoints, not for training jobs6.

E: Using a pre-trained version of the model and running incremental training is not possible, as the DeepAR algorithm does not support incremental training or transfer learning3. The DeepAR algorithm requires a full retraining of the model whenever new data is added or the hyperparameters are changed7.

1: GPU vs CPU: What Matters Most for Machine Learning? | by Louis (What’s AI) Bouchard | Towards Data Science

2: How GPUs Accelerate Machine Learning Training | NVIDIA Developer Blog

3: DeepAR Forecasting Algorithm - Amazon SageMaker

4: Distributed Training - Amazon SageMaker

5: Managed Spot Training - Amazon SageMaker

6: Automatic Scaling - Amazon SageMaker

7: How the DeepAR Algorithm Works - Amazon SageMaker

The best storage scheme for this scenario is to store datasets as files in Amazon S3. Amazon S3 is a scalable, cost-effective, and durable object storage service that can store any amount and type of data. Amazon S3 also supports querying data using SQL with Amazon Athena, a serverless interactive query service that can analyze data directly in S3. This way, the Data Science team can easily explore and analyze their datasets without having to load them into a database or a compute instance.

The other options are not as suitable for this scenario because:

Storing datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance would limit the scalability and availability of the data, as EBS volumes are only accessible within a single availability zone and have a maximum size of 16 TiB. Also, EBS volumes are more expensive than S3 buckets and require provisioning and managing EC2 instances.

Storing datasets as tables in a multi-node Amazon Redshift cluster would incur higher costs and complexity than using S3 and Athena. Amazon Redshift is a data warehouse service that is optimized for analytical queries over structured or semi-structured data. However, it requires setting up and maintaining a cluster of nodes, loading data into tables, and choosing the right distribution and sort keys for optimal performance. Moreover, Amazon Redshift charges for both storage and compute, while S3 and Athena only charge for the amount of data stored and scanned, respectively.

Storing datasets as global tables in Amazon DynamoDB would not be feasible for large amounts of data, as DynamoDB is a key-value and document database service that is designed for fast and consistent performance at any scale. However, DynamoDB has a limit of 400 KB per item and 25 GB per partition key value, which may not be enough for storing large datasets. Also, DynamoDB does not support SQL queries natively, and would require using a service like Amazon EMR or AWS Glue to run SQL queries over DynamoDB data.

Amazon S3 - Cloud Object Storage

Amazon Athena – Interactive SQL Queries for Data in Amazon S3

Amazon EBS - Amazon Elastic Block Store (EBS)

Amazon Redshift – Data Warehouse Solution - AWS

Amazon DynamoDB – NoSQL Cloud Database Service

K-means clustering is a machine learning technique that can be used to classify unlabeled data into different groups based on their similarity. It is an unsupervised learning method, which means it does not require any prior knowledge or labels for the data. K-means clustering works by randomly assigning data points to a number of clusters, then iteratively updating the cluster centers and reassigning the data points until the clusters are stable. The result is a partition of the data into distinct and homogeneous groups. K-means clustering can be useful for exploratory data analysis, data compression, anomaly detection, and feature extraction. References:

K-Means Clustering: A tutorial on how to use K-means clustering with Amazon SageMaker.

Unsupervised Learning: A video that explains the concept and applications of unsupervised learning.

Amazon SageMaker Feature Store is a fully managed, purpose-built repository for storing, updating, and sharing machine learning features. It supports both online and offline stores for features, allowing real-time access for online inference and batch access for offline model training. It also tracks feature history, making it easier for data scientists to work with and access relevant feature sets.

This solution provides the necessary storage and access capabilities with high operational efficiency by managing feature history and enabling controlled access through IAM roles, making it a comprehensive choice for the company’s requirements.

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. SageMaker provides a variety of tools and frameworks to support the entire machine learning workflow, from data preparation to model deployment.

One of the tools that SageMaker offers is the Amazon SageMaker Python SDK, which is a high-level library that simplifies the interaction with SageMaker APIs and services. The SageMaker Python SDK allows you to write code in Python and use popular frameworks such as TensorFlow, PyTorch, MXNet, and more. You can use the SageMaker Python SDK to create and manage SageMaker resources such as notebook instances, training jobs, endpoints, and feature store.

If you need to continue working on a TensorFlow project using SageMaker for training without Wi-Fi access, the best approach is to download the TensorFlow Docker container used in SageMaker from GitHub to your local environment, and use the SageMaker Python SDK to test the code. This way, you can ensure that your code is compatible with the SageMaker environment and avoid any potential issues when you upload your code to SageMaker and start the training job. You can also use the same code to deploy your model to a SageMaker endpoint when you have Wi-Fi access again.

To download the TensorFlow Docker container used in SageMaker, you can visit the SageMaker Docker GitHub repository and follow the instructions to build the image locally. You can also use the SageMaker Studio Image Build CLI to automate the process of building and pushing the Docker image to Amazon Elastic Container Registry (Amazon ECR). To use the SageMaker Python SDK to test the code, you can install the SDK on your local machine by following the installation guide. You can also refer to the TensorFlow documentation for more details on how to use the SageMaker Python SDK with TensorFlow.

SageMaker Docker GitHub repository

SageMaker Studio Image Build CLI

SageMaker Python SDK installation guide

SageMaker Python SDK TensorFlow documentation

The solution that uses Amazon Comprehend and Amazon CloudWatch is the most suitable for the given scenario. Amazon Comprehend is a natural language processing (NLP) service that can analyze text and extract insights such as sentiment, entities, topics, and syntax. Amazon CloudWatch is a monitoring and observability service that can collect and track metrics, create dashboards, and set alarms based on various thresholds. By using these services, the data science team can quickly and easily implement a solution to monitor the sentiment of social media posts without requiring much infrastructure or data science resources. The solution also meets the requirements of storing the sentiment in both S3 and CloudWatch, and using CloudWatch alarms to notify analysts of trends.

Amazon Comprehend

Amazon CloudWatch

The best solution for this scenario is to use a multi-model endpoint in Amazon SageMaker, which allows hosting multiple models on the same endpoint and invoking them dynamically at runtime. This way, the company can reduce the operational overhead of managing multiple EC2 instances and model servers, and leverage the scalability, security, and performance of SageMaker hosting services. By using a multi-model endpoint, the company can also save on hosting costs by improving endpoint utilization and paying only for the models that are loaded in memory and the API calls that are made. To use a multi-model endpoint, the company needs to prepare a Docker container based on the open-source multi-model server, which is a framework-agnostic library that supports loading and serving multiple models from Amazon S3. The company can then create a multi-model endpoint in SageMaker, pointing to the S3 bucket containing all the models, and invoke the endpoint from the web client at runtime, specifying the TargetModel parameter according to the city of each request. This solution also enables the company to add or remove models from the S3 bucket without redeploying the endpoint, and to use different versions of the same model for different cities if needed. References:

Use Docker containers to build models

Host multiple models in one container behind one endpoint

Multi-model endpoints using Scikit Learn

Multi-model endpoints using XGBoost

The Machine Learning team wants to use its own training algorithm on Amazon SageMaker, and submit both its own algorithm code and algorithm-specific parameters. The best combination of services to build a custom algorithm in Amazon SageMaker are Amazon ECR and Amazon S3.

Amazon ECR is a fully managed container registry service that allows you to store, manage, and deploy Docker container images. You can use Amazon ECR to create a Docker image that contains your training algorithm code and any dependencies or libraries that it requires. You can also use Amazon ECR to push, pull, and manage your Docker images securely and reliably.

Amazon S3 is a durable, scalable, and secure object storage service that can store any amount and type of data. You can use Amazon S3 to store your training data, model artifacts, and algorithm-specific parameters. You can also use Amazon S3 to access your data and parameters from your training algorithm code, and to write your model output to a specified location.

Therefore, the Machine Learning team can use the following steps to build a custom algorithm in Amazon SageMaker:

Write the training algorithm code in Python, using the Amazon SageMaker Python SDK or the Amazon SageMaker Containers library to interact with the Amazon SageMaker service. The code should be able to read the input data and parameters from Amazon S3, and write the model output to Amazon S3.

Create a Dockerfile that defines the base image, the dependencies, the environment variables, and the commands to run the training algorithm code. The Dockerfile should also expose the ports that Amazon SageMaker uses to communicate with the container.

Build the Docker image using the Dockerfile, and tag it with a meaningful name and version.

Push the Docker image to Amazon ECR, and note the registry path of the image.

Upload the training data, model artifacts, and algorithm-specific parameters to Amazon S3, and note the S3 URIs of the objects.

Create an Amazon SageMaker training job, using the Amazon SageMaker Python SDK or the AWS CLI. Specify the registry path of the Docker image, the S3 URIs of the input and output data, the algorithm-specific parameters, and other configuration options, such as the instance type, the number of instances, the IAM role, and the hyperparameters.

Monitor the status and logs of the training job, and retrieve the model output from Amazon S3.

Use Your Own Training Algorithms

Amazon ECR - Amazon Web Services

Amazon S3 - Amazon Web Services

For a repository containing a large and dynamically scaling collection of images, Amazon S3 is ideal due to its scalability and versioning capabilities. Amazon S3 natively supports automatic scaling to accommodate increasing storage needs and allows versioning, which enables tracking and managing different versions of objects.

To meet the requirement of maintaining multiple, immediately accessible copies of data across AWS Regions, S3 Cross-Region Replication (CRR) can be enabled. CRR automatically replicates new or updated objects to a specified destination bucket in another AWS Region, ensuring low-latency access and disaster recovery. By setting up CRR with versioning enabled, the data scientist can achieve a multi-Region, scalable, and version-controlled repository in Amazon S3.

XGBoost is a machine learning framework that can be used for classification, regression, ranking, and other tasks. It is based on the gradient boosting algorithm, which builds an ensemble of weak learners (usually decision trees) to produce a strong learner. XGBoost has several advantages over other algorithms, such as scalability, parallelization, regularization, and sparsity handling. For categorizing new products using the provided dataset, an XGBoost model would be a suitable choice, because it can handle multiple features and multiple classes efficiently and accurately. To train an XGBoost model for multi-class classification, the objective parameter should be set to multi: softmax, which means that the model will output a probability distribution over the classes and predict the class with the highest probability. Alternatively, the objective parameter can be set to multi: softprob, which means that the model will output the raw probability of each class instead of the predicted class label. This can be useful for evaluating the model performance or for post-processing the predictions. References:

XGBoost: A tutorial on how to use XGBoost with Amazon SageMaker.

XGBoost Parameters: A reference guide for the parameters of XGBoost.

 The best model for predicting housing prices based on a historical dataset with 32 features is linear regression. Linear regression is a supervised learning algorithm that fits a linear relationship between a dependent variable (housing price) and one or more independent variables (features). Linear regression can handle multiple features and output a continuous value for the housing price. Linear regression can also return the coefficients of the features, which indicate how each feature affects the housing price. Linear regression is suitable for this problem because the outcome of interest is numerical and continuous, and the model needs to capture the linear relationship between the features and the outcome.

AWS Machine Learning Specialty Exam Guide

AWS Machine Learning Training - Regression vs Classification in Machine Learning

AWS Machine Learning Training - Linear Regression with Amazon SageMaker

 Residual plots are a model evaluation technique that can be used to understand whether a regression model is more frequently overestimating or underestimating the target. Residual plots are graphs that plot the residuals (the difference between the actual and predicted values) against the predicted values or other variables. Residual plots can help the Machine Learning Specialist to identify the patterns and trends in the residuals, such as the direction, shape, and distribution. Residual plots can also reveal the presence of outliers, heteroscedasticity, non-linearity, or other problems in the model12

To determine whether the model is overestimating or underestimating the target, the Machine Learning Specialist can use a residual plot that plots the residuals against the predicted values. This type of residual plot is also known as a prediction error plot. A prediction error plot can show the magnitude and direction of the errors made by the model. If the model is overestimating the target, the residuals will be negative, and the points will be below the zero line. If the model is underestimating the target, the residuals will be positive, and the points will be above the zero line. If the model is accurate, the residuals will be close to zero, and the points will be scattered around the zero line. A prediction error plot can also show the variance and bias of the model. If the model has high variance, the residuals will have a large spread, and the points will be far from the zero line. If the model has high bias, the residuals will have a systematic pattern, such as a curve or a slope, and the points will not be randomly distributed around the zero line. A prediction error plot can help the Machine Learning Specialist to optimize the model by adjusting the complexity, features, or parameters of the model34

The other options are not valid or suitable for determining whether the model is overestimating or underestimating the target. Root Mean Square Error (RMSE) is a model evaluation metric that measures the average magnitude of the errors made by the model. RMSE is the square root of the mean of the squared residuals. RMSE can indicate the overall accuracy and performance of the model, but it cannot show the direction or distribution of the errors. RMSE can also be influenced by outliers or extreme values, and it may not be comparable across different models or datasets5 Area under the curve (AUC) is a model evaluation metric that measures the ability of the model to distinguish between the positive and negative classes. AUC is the area under the receiver operating characteristic (ROC) curve, which plots the true positive rate against the false positive rate for various classification thresholds. AUC can indicate the overall quality and performance of the model, but it is only applicable for binary classification models, not regression models. AUC cannot show the magnitude or direction of the errors made by the model. Confusion matrix is a model evaluation technique that summarizes the number of correct and incorrect predictions made by the model for each class. A confusion matrix is a table that shows the counts of true positives, false positives, true negatives, and false negatives for each class. A confusion matrix can indicate the accuracy, precision, recall, and F1-score of the model for each class, but it is only applicable for classification models, not regression models. A confusion matrix cannot show the magnitude or direction of the errors made by the model.

In linear least squares regression, the design matrix (often denoted as XXX) must have full rank to ensure a unique solution. When two or more features are perfectly linearly dependent, it leads to multicollinearity, causing the matrix XTXX^TXXTX to become singular (non-invertible). This singularity prevents the computation of a unique solution for the regression coefficients.​

From the documentation:​

Therefore, perfect linear dependence among features is problematic for linear regression models as it undermines the mathematical foundation required for deriving unique solutions.

The combination of steps that the data scientist must take to perform the A/B testing are to create a new endpoint configuration that includes a production variant for each of the two models, and update the existing endpoint to use the new endpoint configuration. This approach will allow the data scientist to deploy both models on the same endpoint and split the inference traffic between them based on a specified distribution.

Amazon SageMaker is a fully managed service that provides developers and data scientists the ability to quickly build, train, and deploy machine learning models. Amazon SageMaker supports A/B testing on machine learning models by allowing the data scientist to run multiple production variants on an endpoint. A production variant is a version of a model that is deployed on an endpoint. Each production variant has a name, a machine learning model, an instance type, an initial instance count, and an initial weight. The initial weight determines the percentage of inference requests that the variant will handle. For example, if there are two variants with weights of 0.5 and 0.5, each variant will handle 50% of the requests. The data scientist can use production variants to test models that have been trained using different training datasets, algorithms, and machine learning frameworks; test how they perform on different instance types; or a combination of all of the above1.

To perform A/B testing on machine learning models, the data scientist needs to create a new endpoint configuration that includes a production variant for each of the two models. An endpoint configuration is a collection of settings that define the properties of an endpoint, such as the name, the production variants, and the data capture configuration. The data scientist can use the Amazon SageMaker console, the AWS CLI, or the AWS SDKs to create a new endpoint configuration. The data scientist needs to specify the name, model name, instance type, initial instance count, and initial variant weight for each production variant in the endpoint configuration2.

After creating the new endpoint configuration, the data scientist needs to update the existing endpoint to use the new endpoint configuration. Updating an endpoint is the process of deploying a new endpoint configuration to an existing endpoint. Updating an endpoint does not affect the availability or scalability of the endpoint, as Amazon SageMaker creates a new endpoint instance with the new configuration and switches the DNS record to point to the new instance when it is ready. The data scientist can use the Amazon SageMaker console, the AWS CLI, or the AWS SDKs to update an endpoint. The data scientist needs to specify the name of the endpoint and the name of the new endpoint configuration to update the endpoint3.

The other options are either incorrect or unnecessary. Creating a new endpoint configuration that includes two target variants that point to different endpoints is not possible, as target variants are only used to invoke a specific variant on an endpoint, not to define an endpoint configuration. Deploying the new model to the existing endpoint would replace the existing model, not run it side-by-side with the new model. Updating the existing endpoint to activate the new model is not a valid operation, as there is no activation parameter for an endpoint.

1: A/B Testing ML models in production using Amazon SageMaker | AWS Machine Learning Blog

2: Create an Endpoint Configuration - Amazon SageMaker

3: Update an Endpoint - Amazon SageMaker

 The ML specialist should run the Model Monitor baseline job again on the new training set and configure Model Monitor to use the new baseline. This is because the baseline job computes the statistics and constraints for the data quality and model quality metrics, which are used to detect violations. If the training set changes, the baseline job should be updated accordingly to reflect the new distribution of the data and the model performance. Otherwise, the old baseline may not be representative of the current production traffic and may cause false alarms or miss violations. References:

Monitor data and model quality - Amazon SageMaker

Detecting and analyzing incorrect model predictions with Amazon SageMaker Model Monitor and Debugger | AWS Machine Learning Blog

Amazon Redshift ML enables in-database machine learning model creation and predictions, allowing data scientists to leverage Redshift for model training without needing to export data.

To create and run a model for customer churn prediction in Amazon Redshift ML:

Define the feature variables and target variable: Identify the columns to use as features (predictors) and the target variable (outcome) for the churn prediction model.

Create the model: Write a CREATE MODEL SQL statement, which trains the model using Amazon Redshift’s integration with Amazon SageMaker and stores the model directly in Redshift.

Run predictions: Use the SQL PREDICT function to generate predictions on new data directly within Redshift.

Options B, D, and E are not required as Redshift ML handles model creation and prediction without manual data export to Amazon S3 or additional Spectrum integration.

Amazon Kinesis Data Analytics is a service that enables you to analyze streaming data in real time using SQL or Apache Flink applications. You can use Kinesis Data Analytics to process and gain insights from data streams such as web logs, clickstreams, IoT data, and more.

To use SQL to query a data stream of GZIP files, you need to first transform the data into a format that Kinesis Data Analytics can understand, such as JSON, CSV, or Apache Parquet. You can use an AWS Lambda function to perform this transformation and send the output to a Kinesis data stream that is connected to your Kinesis Data Analytics application. This way, you can use SQL to query the stream with the least latency, as Lambda functions are triggered in near real time by the incoming data and Kinesis Data Analytics can process the data as soon as it arrives.

The other options are not optimal for this scenario, as they introduce more latency or complexity. AWS Glue is a serverless data integration service that can perform ETL (extract, transform, and load) tasks on data sources, but it is not designed for real-time streaming data analysis. An Amazon Kinesis Client Library is a Java library that enables you to build custom applications that process data from Kinesis data streams, but it requires more coding and configuration than using a Lambda function. Amazon Kinesis Data Firehose is a service that can deliver streaming data to destinations such as Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and Splunk, but it does not support SQL queries on the data.

What Is Amazon Kinesis Data Analytics for SQL Applications?

Using AWS Lambda with Amazon Kinesis Data Streams

Using AWS Lambda with Amazon Kinesis Data Firehose

The data scientist should use Amazon SageMaker Ground Truth to sort the data into two groups named “enrolled” or “not enrolled.” This will create a labeled dataset that can be used for supervised learning. The data scientist should then use a classification algorithm to run predictions on the test data. A classification algorithm is a suitable choice for predicting a binary outcome, such as enrollment status, based on the input features, such as academic performance. A classification algorithm will output a probability for each class label and assign the most likely label to each observation.

Use Amazon SageMaker Ground Truth to Label Data

Classification Algorithm in Machine Learning

Area Under the ROC Curve (AUC) is a metric that is best suited to score the model for the given scenario. AUC is a measure of the performance of a binary classifier, such as a model that predicts whether a credit card transaction is valid or fraudulent. AUC is calculated based on the Receiver Operating Characteristic (ROC) curve, which is a plot that shows the trade-off between the true positive rate (TPR) and the false positive rate (FPR) of the classifier as the decision threshold is varied. The TPR, also known as recall or sensitivity, is the proportion of actual positive cases (fraudulent transactions) that are correctly predicted as positive by the classifier. The FPR, also known as the fall-out, is the proportion of actual negative cases (valid transactions) that are incorrectly predicted as positive by the classifier. The ROC curve illustrates how well the classifier can distinguish between the two classes, regardless of the class distribution or the error costs. A perfect classifier would have a TPR of 1 and an FPR of 0 for all thresholds, resulting in a ROC curve that goes from the bottom left to the top left and then to the top right of the plot. A random classifier would have a TPR and an FPR that are equal for all thresholds, resulting in a ROC curve that goes from the bottom left to the top right of the plot along the diagonal line. AUC is the area under the ROC curve, and it ranges from 0 to 1. A higher AUC indicates a better classifier, as it means that the classifier has a higher TPR and a lower FPR for all thresholds. AUC is a useful metric for imbalanced classification problems, such as the credit card transaction dataset, because it is insensitive to the class imbalance and the error costs. AUC can capture the overall performance of the classifier across all possible scenarios, and it can be used to compare different classifiers based on their ROC curves.

The other options are not as suitable as AUC for the given scenario for the following reasons:

Precision: Precision is the proportion of predicted positive cases (fraudulent transactions) that are actually positive. Precision is a useful metric when the cost of a false positive is high, such as in spam detection or medical diagnosis. However, precision is not a good metric for imbalanced classification problems, because it can be misleadingly high when the positive class is rare. For example, a classifier that predicts all transactions as valid would have a precision of 0, but a very high accuracy of 99%. Precision is also dependent on the decision threshold and the error costs, which may vary for different scenarios.

Recall: Recall is the same as the TPR, and it is the proportion of actual positive cases (fraudulent transactions) that are correctly predicted as positive by the classifier. Recall is a useful metric when the cost of a false negative is high, such as in fraud detection or cancer diagnosis. However, recall is not a good metric for imbalanced classification problems, because it can be misleadingly low when the positive class is rare. For example, a classifier that predicts all transactions as fraudulent would have a recall of 1, but a very low accuracy of 1%. Recall is also dependent on the decision threshold and the error costs, which may vary for different scenarios.

Root Mean Square Error (RMSE): RMSE is a metric that measures the average difference between the predicted and the actual values. RMSE is a useful metric for regression problems, where the goal is to predict a continuous value, such as the price of a house or the temperature of a city. However, RMSE is not a good metric for classification problems, where the goal is to predict a discrete value, such as the class label of a transaction. RMSE is not meaningful for classification problems, because it does not capture the accuracy or the error costs of the predictions.

ROC Curve and AUC

How and When to Use ROC Curves and Precision-Recall Curves for Classification in Python

Precision-Recall

Root Mean Squared Error

AWS Glue DataBrew provides a no-code data preparation interface that enables business analysts to clean and transform data from various sources, including PostgreSQL databases, without needing programming skills. Amazon SageMaker Canvas offers a no-code interface for machine learning model training and predictions, allowing users to predict future production volume without coding expertise.

This solution meets the requirements efficiently by providing end-to-end data preparation and prediction modeling without requiring coding.

The combination of steps that will meet the requirements are to create an IAM role in the development account that the integration account and production account can assume, attach IAM policies to the role that allow access to the feature repository and the S3 buckets, and share the feature repository that is associated with the S3 buckets from the development account to the integration account and the production account by using AWS Resource Access Manager (AWS RAM). This approach will enable cross-account access and sharing of the features stored in Amazon SageMaker Feature Store and Amazon S3.

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, update, search, and share curated data used in training and prediction workflows. The service provides feature management capabilities such as enabling easy feature reuse, low latency serving, time travel, and ensuring consistency between features used in training and inference workflows. A feature group is a logical grouping of ML features whose organization and structure is defined by a feature group schema. A feature group schema consists of a list of feature definitions, each of which specifies the name, type, and metadata of a feature. Amazon SageMaker Feature Store stores the features in both an online store and an offline store. The online store is a low-latency, high-throughput store that is optimized for real-time inference. The offline store is a historical store that is backed by an Amazon S3 bucket and is optimized for batch processing and model training1.

AWS Identity and Access Management (IAM) is a web service that helps you securely control access to AWS resources for your users. You use IAM to control who can use your AWS resources (authentication) and what resources they can use and in what ways (authorization). An IAM role is an IAM identity that you can create in your account that has specific permissions. You can use an IAM role to delegate access to users, applications, or services that don’t normally have access to your AWS resources. For example, you can create an IAM role in your development account that allows the integration account and the production account to assume the role and access the resources in the development account. You can attach IAM policies to the role that specify the permissions for the feature repository and the S3 buckets. You can also use IAM conditions to restrict the access based on the source account, IP address, or other factors2.

AWS Resource Access Manager (AWS RAM) is a service that enables you to easily and securely share AWS resources with any AWS account or within your AWS Organization. You can share AWS resources that you own with other accounts using resource shares. A resource share is an entity that defines the resources that you want to share, and the principals that you want to share with. For example, you can share the feature repository that is associated with the S3 buckets from the development account to the integration account and the production account by creating a resource share in AWS RAM. You can specify the feature group ARN and the S3 bucket ARN as the resources, and the integration account ID and the production account ID as the principals. You can also use IAM policies to further control the access to the shared resources3.

The other options are either incorrect or unnecessary. Using AWS Security Token Service (AWS STS) from the integration account and the production account to retrieve credentials for the development account is not required, as the IAM role in the development account can provide temporary security credentials for the cross-account access. Setting up S3 replication between the development S3 buckets and the integration and production S3 buckets would introduce redundancy and inconsistency, as the S3 buckets are already shared through AWS RAM. Creating an AWS PrivateLink endpoint in the development account for SageMaker is not relevant, as it is used to securely connect to SageMaker services from a VPC, not from another account.

1: Amazon SageMaker Feature Store – Amazon Web Services

2: What Is IAM? - AWS Identity and Access Management

3: What Is AWS Resource Access Manager? - AWS Resource Access Manager

To build a scalable, serverless, and cost-effective data ingestion pipeline, this solution uses a Kinesis data stream to handle fluctuations in data flow, buffering and distributing incoming data in real time. By connecting two Amazon Kinesis Data Firehose delivery streams to the Kinesis data stream, the company can simultaneously route data to Amazon S3 for backup and Amazon OpenSearch Service for analysis.

This approach meets all requirements by providing automatic scaling, reducing operational overhead, and ensuring data storage and analysis without duplicating efforts or needing additional infrastructure.

The best solution for this scenario is to use shadow deployment, which is a technique that allows the company to run the new experimental model in parallel with the existing model, without exposing it to the end users. In shadow deployment, the company can route the same user requests to both models, but only return the responses from the existing model to the users. The responses from the new experimental model are logged and analyzed for quality and performance metrics, such as accuracy, latency, and resource consumption12. This way, the company can validate the new experimental model in a production environment, without affecting the current live traffic or user experience.

The other solutions are not suitable, because they have the following drawbacks:

A: A/B testing is a technique that involves splitting the user traffic between two or more models, and comparing their outcomes based on predefined metrics. However, this technique exposes the new experimental model to a portion of the end users, which might affect their experience if the model is not reliable or consistent with the existing model3.

B: Canary release is a technique that involves gradually rolling out the new experimental model to a small subset of users, and monitoring its performance and feedback. However, this technique also exposes the new experimental model to some end users, and requires careful selection and segmentation of the user groups4.

D: Blue/green deployment is a technique that involves switching the user traffic from the existing model (blue) to the new experimental model (green) at once, after testing and verifying the new model in a separate environment. However, this technique does not allow the company to validate the new experimental model in a production environment, and might cause service disruption or inconsistency if the new model is not compatible or stable5.

1: Shadow Deployment: A Safe Way to Test in Production | LaunchDarkly Blog

2: Shadow Deployment: A Safe Way to Test in Production | LaunchDarkly Blog

3: A/B Testing for Machine Learning Models | AWS Machine Learning Blog

4: Canary Releases for Machine Learning Models | AWS Machine Learning Blog

5: Blue-Green Deployments for Machine Learning Models | AWS Machine Learning Blog

The SageMaker Variant Invocations Per Instance setting is the target value for the average number of invocations per instance per minute for the model variant. It is used by the automatic scaling policy to add or remove instances to keep the metric close to the specified value. To determine this value, the following equation can be used in combination with load testing:

SageMakerVariantInvocationsPerInstance = (MAX_RPS * SAFETY_FACTOR) * 60

Where MAX_RPS is the maximum requests per second that the model variant can handle without service degradation, SAFETY_FACTOR is a factor that ensures that the clients do not exceed the maximum RPS, and 60 is the conversion factor from seconds to minutes. In this case, the given parameters are:

MAX_RPS = 20 SAFETY_FACTOR = 0.5

Plugging these values into the equation, we get:

SageMakerVariantInvocationsPerInstance = (20 * 0.5) * 60 SageMakerVariantInvocationsPerInstance = 600

Therefore, the Specialist should set the SageMaker Variant Invocations Per Instance setting to 600.

Load testing your auto scaling configuration - Amazon SageMaker

Configure model auto scaling with the console - Amazon SageMaker

 Amazon EMR is a service that can process large amounts of data efficiently and cost-effectively. It can run distributed frameworks such as Apache Spark, which can perform feature engineering on big data. Amazon SageMaker SDK is a Python library that can interact with Amazon SageMaker service to train and deploy machine learning models. It can also use Amazon EMR as a data source for training data. References:

Amazon EMR

Amazon SageMaker SDK

The Data Scientist should increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights and change the XGBoost eval_metric parameter to optimize based on Area Under the ROC Curve (AUC). This will help reduce the number of false negative predictions by the model.

The scale_pos_weight parameter controls the balance of positive and negative weights in the XGBoost algorithm. It is useful for imbalanced classification problems, such as fraud detection, where the number of positive examples (fraudulent transactions) is much smaller than the number of negative examples (non-fraudulent transactions). By increasing the scale_pos_weight parameter, the Data Scientist can assign more weight to the positive class and make the model more sensitive to detecting fraudulent transactions.

The eval_metric parameter specifies the metric that is used to measure the performance of the model during training and validation. The default metric for binary classification problems is the error rate, which is the fraction of incorrect predictions. However, the error rate is not a good metric for imbalanced classification problems, because it does not take into account the cost of different types of errors. For example, in fraud detection, a false negative (failing to detect a fraudulent transaction) is more costly than a false positive (flagging a non-fraudulent transaction as fraudulent). Therefore, the Data Scientist should use a metric that reflects the trade-off between the true positive rate (TPR) and the false positive rate (FPR), such as the Area Under the ROC Curve (AUC). The AUC is a measure of how well the model can distinguish between the positive and negative classes, regardless of the classification threshold. A higher AUC means that the model can achieve a higher TPR with a lower FPR, which is desirable for fraud detection.

XGBoost Parameters - Amazon Machine Learning

Using XGBoost with Amazon SageMaker - AWS Machine Learning Blog

Forecasting is a type of machine learning model that predicts future values of a target variable based on historical data and other features. Forecasting is suitable for problems that involve time-series data, such as the number of claims in each category from month to month. Forecasting can handle multiple categories of the target variable, as well as missing or partial information on some features. Therefore, option C is the best choice for the given problem.

Option A is incorrect because classification is a type of machine learning model that assigns a label to an input based on predefined categories. Classification is not suitable for predicting continuous or numerical values, such as the number of claims in each category from month to month. Moreover, classification requires sufficient and complete information on the features that are relevant to the target variable, which is not the case for the given problem. Option B is incorrect because reinforcement learning is a type of machine learning model that learns from its own actions and rewards in an interactive environment. Reinforcement learning is not suitable for problems that involve historical data and do not require an agent to take actions. Option D is incorrect because it combines two different types of machine learning models, which is unnecessary and inefficient. Moreover, classification is not suitable for predicting the number of claims in some categories, as explained in option A.

Forecasting | AWS Solutions for Machine Learning (AI/ML) | AWS Solutions Library

Time Series Forecasting Service – Amazon Forecast – Amazon Web Services

Amazon Forecast: Guide to Predicting Future Outcomes - Onica

Amazon Launches What-If Analyses for Machine Learning Forecasting …

 A precision-recall curve is a plot that shows the trade-off between the precision and recall of a binary classifier as the decision threshold is varied. It is a useful tool for evaluating and comparing the performance of different models. To generate a precision-recall curve, the following steps are needed:

Calculate the precision and recall values for different threshold values using the predictions and the true labels of the data.

Plot the precision values on the y-axis and the recall values on the x-axis for each threshold value.

Optionally, calculate the area under the curve (AUC) as a summary metric of the model performance.

Among the four options, option C requires the least coding effort to generate and share a visualization of the daily precision-recall curve from the predictions. This option involves the following steps:

Run a daily Amazon EMR workflow to generate precision-recall data: Amazon EMR is a service that allows running big data frameworks, such as Apache Spark, on a managed cluster of EC2 instances. Amazon EMR can handle large-scale data processing and analysis, such as calculating the precision and recall values for different threshold values from 100 TB of predictions. Amazon EMR supports various languages, such as Python, Scala, and R, for writing the code to perform the calculations. Amazon EMR also supports scheduling workflows using Apache Airflow or AWS Step Functions, which can automate the daily execution of the code.

Save the results in Amazon S3: Amazon S3 is a service that provides scalable, durable, and secure object storage. Amazon S3 can store the precision-recall data generated by Amazon EMR in a cost-effective and accessible way. Amazon S3 supports various data formats, such as CSV, JSON, or Parquet, for storing the data. Amazon S3 also integrates with other AWS services, such as Amazon QuickSight, for further processing and visualization of the data.

Visualize the arrays in Amazon QuickSight: Amazon QuickSight is a service that provides fast, easy-to-use, and interactive business intelligence and data visualization. Amazon QuickSight can connect to Amazon S3 as a data source and import the precision-recall data into a dataset. Amazon QuickSight can then create a line chart to plot the precision-recall curve from the dataset. Amazon QuickSight also supports calculating the AUC and adding it as an annotation to the chart.

Publish them in a dashboard shared with the Business team: Amazon QuickSight allows creating and publishing dashboards that contain one or more visualizations from the datasets. Amazon QuickSight also allows sharing the dashboards with other users or groups within the same AWS account or across different AWS accounts. The Business team can access the dashboard with read-only permissions and view the daily precision-recall curve from the predictions.

The other options require more coding effort than option C for the following reasons:

Option A: This option requires writing code to plot the precision-recall curve from the data stored in Amazon S3, as well as creating a mechanism to share the plot with the Business team. This can involve using additional libraries or tools, such as matplotlib, seaborn, or plotly, for creating the plot, and using email, web, or cloud services, such as AWS Lambda or Amazon SNS, for sharing the plot.

Option B: This option requires transforming the predictions into a format that Amazon QuickSight can recognize and import as a data source, such as CSV, JSON, or Parquet. This can involve writing code to process and convert the predictions, as well as uploading them to a storage service, such as Amazon S3 or Amazon Redshift, that Amazon QuickSight can connect to.

Option D: This option requires writing code to generate precision-recall data in Amazon ES, as well as creating a dashboard to visualize the data. Amazon ES is a service that provides a fully managed Elasticsearch cluster, which is mainly used for search and analytics purposes. Amazon ES is not designed for generating precision-recall data, and it requires using a specific data format, such as JSON, for storing the data. Amazon ES also requires using a tool, such as Kibana, for creating and sharing the dashboard, which can involve additional configuration and customization steps.

Precision-Recall

What Is Amazon EMR?

What Is Amazon S3?

[What Is Amazon QuickSight?]

[What Is Amazon Elasticsearch Service?]

The company should configure Amazon Textract to route low-confidence predictions to Amazon Augmented AI (Amazon A2I). Amazon A2I is a service that allows you to implement human review of machine learning (ML) predictions. It also comes integrated with some of the Artificial Intelligence (AI) services such as Amazon Textract. By using Amazon A2I, the company can perform a manual review on those words that have low confidence scores before performing a business validation. This will help reduce the processing time of loan applications by avoiding errors and rework.

Option A is incorrect because Amazon SageMaker Ground Truth is not a suitable service for human review of Amazon Textract predictions. Amazon SageMaker Ground Truth is a service that helps you build highly accurate training datasets for machine learning. It allows you to label your own data or use a workforce of human labelers. However, it does not provide an easy way to integrate with Amazon Textract and route low-confidence predictions for human review.

Option B is incorrect because using an Amazon Textract synchronous operation instead of an asynchronous operation will not reduce the processing time of loan applications. A synchronous operation is a request-response operation that returns the results immediately. An asynchronous operation is a start-and-check operation that returns a job identifier that you can use to check the status and results later. The choice of operation depends on the size and complexity of the document, not on the confidence of the predictions.

Option D is incorrect because using Amazon Rekognition’s feature to detect text in an image to extract the data from scanned images is not a better alternative than using Amazon Textract. Amazon Rekognition is a service that provides computer vision capabilities, such as face recognition, object detection, and scene analysis. It can also detect text in an image, but it does not provide the same level of accuracy and functionality as Amazon Textract. Amazon Textract can not only detect text, but also extract data from tables and forms, and understand the layout and structure of the document.

Amazon Augmented AI

Amazon SageMaker Ground Truth

Amazon Textract Operations

Amazon Rekognition

The Data Scientist is building a model to predict customer churn using a dataset of 100 continuous numerical features. The Marketing team wants to interpret the model and see the direct impact of relevant features on the model outcome. However, the Data Scientist observes that there is a wide gap between the training and validation set accuracy, which indicates that the model is overfitting the data and generalizing poorly to new data.

To improve the model performance and satisfy the Marketing team’s needs, the Data Scientist can use the following methods:

Add L1 regularization to the classifier: L1 regularization is a technique that adds a penalty term to the loss function of the logistic regression model, proportional to the sum of the absolute values of the coefficients. L1 regularization can help reduce overfitting by shrinking the coefficients of the less important features to zero, effectively performing feature selection. This can simplify the model and make it more interpretable, as well as improve the validation accuracy.

Perform recursive feature elimination: Recursive feature elimination (RFE) is a feature selection technique that involves training a model on a subset of the features, and then iteratively removing the least important features one by one until the desired number of features is reached. The idea behind RFE is to determine the contribution of each feature to the model by measuring how well the model performs when that feature is removed. The features that are most important to the model will have the greatest impact on performance when they are removed. RFE can help improve the model performance by eliminating the irrelevant or redundant features that may cause noise or multicollinearity in the data. RFE can also help the Marketing team understand the direct impact of the relevant features on the model outcome, as the remaining features will have the highest weights in the model.

Regularization for Logistic Regression

Recursive Feature Elimination

The best way to ensure that provisioned resources are being utilized effectively is to redeploy the model on an M5 instance and attach Amazon Elastic Inference to the instance. Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances to reduce the cost of running deep learning inference by up to 75%. By using Amazon Elastic Inference, you can choose the instance type that is best suited to the overall CPU and memory needs of your application, and then separately configure the amount of inference acceleration that you need with no code changes. This way, you can avoid wasting GPU resources and pay only for what you use.

Option A is incorrect because a batch transform job is not suitable for real-time predictions. Batch transform is a high-performance and cost-effective feature for generating inferences using your trained models. Batch transform manages all of the compute resources required to get inferences. Batch transform is ideal for scenarios where you’re working with large batches of data, don’t need sub-second latency, or need to process data that is stored in Amazon S3.

Option C is incorrect because redeploying the model on a P3dn instance would not improve the resource utilization. P3dn instances are designed for distributed machine learning and high performance computing applications that need high network throughput and packet rate performance. They are not optimized for inference workloads.

Option D is incorrect because deploying the model onto an Amazon ECS cluster using a P3 instance would not ensure that provisioned resources are being utilized effectively. Amazon ECS is a fully managed container orchestration service that allows you to run and scale containerized applications on AWS. However, using Amazon ECS would not address the issue of underutilized GPU resources. In fact, it might introduce additional overhead and complexity in managing the cluster.

Amazon Elastic Inference - Amazon SageMaker

Batch Transform - Amazon SageMaker

Amazon EC2 P3 Instances

Amazon EC2 P3dn Instances

Amazon Elastic Container Service

The best storage option for the trucking company is to use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies. A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. Amazon S3 is the ideal choice for building a data lake because it offers high durability, scalability, availability, and security. You can store any type of data in Amazon S3, such as images, videos, audio, text, etc. You can also use AWS services such as Amazon Rekognition, Amazon SageMaker, and Amazon EMR to analyze and process the data in the data lake. To ensure the data is only accessible to specific IAM users, you can use bucket policies to grant or deny access to the S3 buckets based on the IAM user’s identity or role. Bucket policies are JSON documents that specify the permissions for the bucket and the objects in it. You can use conditions to restrict access based on various factors, such as IP address, time, source, etc. By using bucket policies, you can control who can access the data in the data lake and what actions they can perform on it.

AWS Machine Learning Specialty Exam Guide

AWS Machine Learning Training - Build a Data Lake Foundation with Amazon S3

AWS Machine Learning Training - Using Bucket Policies and User Policies

The solution C meets the requirements because it minimizes costs for compute infrastructure, maximizes the scalability of resources for training, and facilitates the use of an ML model in low-connectivity environments. The solution C involves the following steps:

Move the training data to an Amazon S3 bucket. This will enable the company to store the large amount of data in a durable, scalable, and cost-effective way. It will also allow the company to access the data from the cloud for training and evaluation purposes1.

Train and evaluate the model by using Amazon SageMaker. This will enable the company to use a fully managed service that provides various features and tools for building, training, tuning, and deploying ML models. Amazon SageMaker can handle large-scale data processing and distributed training, and it can leverage the power of AWS compute resources such as Amazon EC2, Amazon EKS, and AWS Fargate2.

Optimize the model by using SageMaker Neo. This will enable the company to reduce the size of the model and improve its performance and efficiency. SageMaker Neo can compile the model into an executable that can run on various hardware platforms, such as CPUs, GPUs, and edge devices3.

Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. This will enable the company to deploy the model on a local device that can run inference in real time, even in low-connectivity environments. AWS IoT Greengrass can extend AWS cloud capabilities to the edge, and it can securely communicate with the cloud for updates and synchronization4.

Deploy the model on the edge device. This will enable the company to automate quality control in its facilities by using the model to detect defects in new parts as they move on a conveyor belt. The model can run inference locally on the edge device without requiring internet connectivity, and it can send the results to the cloud when the connection is available4.

The other options are not suitable because:

Option A: Deploying the model on a SageMaker hosting services endpoint will not facilitate the use of the model in low-connectivity environments, as it will require internet access to perform inference. Moreover, it may incur higher costs for hosting and data transfer than deploying the model on an edge device.

Option B: Training and evaluating the model on premises will not minimize costs for compute infrastructure, as it will require the company to maintain and upgrade its own hardware and software. Moreover, it will not maximize the scalability of resources for training, as it will limit the company’s ability to leverage the cloud’s elasticity and flexibility.

Option D: Training the model on premises will not minimize costs for compute infrastructure, nor maximize the scalability of resources for training, for the same reasons as option B.

1: Amazon S3

2: Amazon SageMaker

3: SageMaker Neo

4: AWS IoT Greengrass

 A deep convolutional neural network (CNN) classifier is a suitable architecture for image classification tasks, as it can learn features from the images and reduce the dimensionality of the input. A linear output layer that outputs the probability that an image contains a car is appropriate for a binary classification problem, as it can produce a single scalar value between 0 and 1. A softmax output layer is more suitable for a multi-class classification problem, as it can produce a vector of probabilities that sum up to 1. A deep multilayer perceptron (MLP) classifier is not as effective as a CNN for image classification, as it does not exploit the spatial structure of the images and requires a large number of parameters to process the high-dimensional input. References:

AWS Certified Machine Learning - Specialty Exam Guide

AWS Training - Machine Learning on AWS

AWS Whitepaper - An Overview of Machine Learning on AWS

The solution C is the best option to identify and address training issues with the least development effort. The solution C involves the following steps:

Use the SageMaker Debugger vanishing_gradient and LowGPUUtilization built-in rules to detect issues. SageMaker Debugger is a feature of Amazon SageMaker that allows data scientists to monitor, analyze, and debug machine learning models during training. SageMaker Debugger provides a set of built-in rules that can automatically detect common issues and anomalies in model training, such as vanishing or exploding gradients, overfitting, underfitting, low GPU utilization, and more1. The data scientist can use the vanishing_gradient rule to check if the gradients are becoming too small and causing the training to not converge. The data scientist can also use the LowGPUUtilization rule to check if the GPU resources are underutilized and causing the training to be inefficient2.

Launch the StopTrainingJob action if issues are detected. SageMaker Debugger can also take actions based on the status of the rules. One of the actions is StopTrainingJob, which can terminate the training job if a rule is in an error state. This can help the data scientist to save time and money by stopping the training early if issues are detected3.

The other options are not suitable because:

Option A: Using CPU utilization metrics that are captured in Amazon CloudWatch and configuring a CloudWatch alarm to stop the training job early if low CPU utilization occurs will not identify and address training issues effectively. CPU utilization is not a good indicator of model training performance, especially for GPU instances. Moreover, CloudWatch alarms can only trigger actions based on simple thresholds, not complex rules or conditions4.

Option B: Using high-resolution custom metrics that are captured in Amazon CloudWatch and configuring an AWS Lambda function to analyze the metrics and to stop the training job early if issues are detected will incur more development effort than using SageMaker Debugger. The data scientist will have to write the code for capturing, sending, and analyzing the custom metrics, as well as for invoking the Lambda function and stopping the training job. Moreover, this solution may not be able to detect all the issues that SageMaker Debugger can5.

Option D: Using the SageMaker Debugger confusion and feature_importance_overweight built-in rules and launching the StopTrainingJob action if issues are detected will not identify and address training issues effectively. The confusion rule is used to monitor the confusion matrix of a classification model, which is not relevant for a regression model that predicts prices. The feature_importance_overweight rule is used to check if some features have too much weight in the model, which may not be related to the convergence or resource utilization issues2.

1: Amazon SageMaker Debugger

2: Built-in Rules for Amazon SageMaker Debugger

3: Actions for Amazon SageMaker Debugger

4: Amazon CloudWatch Alarms

5: Amazon CloudWatch Custom Metrics

Regularization is a technique that can be used to prevent overfitting and improve model performance on unseen data. Overfitting occurs when the model learns the training data too well and fails to generalize to new and unseen data. This can be seen in the question, where the validation accuracy is lower than the training accuracy, and both are lower than the human-level performance. Regularization is a way of adding some constraints or penalties to the model to reduce its complexity and prevent it from memorizing the training data. Some common forms of regularization for image classification are:

Weight decay: Adding a term to the loss function that penalizes large weights in the model. This can help reduce the variance and noise in the model and make it more robust to small changes in the input.

Dropout: Randomly dropping out some units or connections in the model during training. This can help reduce the co-dependency among the units and make the model more resilient to missing or corrupted features.

Data augmentation: Artificially increasing the size and diversity of the training data by applying random transformations, such as cropping, flipping, rotating, scaling, etc. This can help the model learn more invariant and generalizable features and reduce the risk of overfitting to specific patterns in the training data.

The other options are not likely to fix the issue of overfitting, and may even worsen it:

A longer training time: This can lead to more overfitting, as the model will have more chances to fit the noise and details in the training data that are not relevant for the validation data.

Making the network larger: This can increase the model capacity and complexity, which can also lead to more overfitting, as the model will have more parameters to learn and adjust to the training data.

Using a different optimizer: This can affect the speed and stability of the training process, but not necessarily the generalization ability of the model. The choice of optimizer depends on the characteristics of the data and the model, and there is no guarantee that a different optimizer will prevent overfitting.

Regularization (machine learning)

Image Classification: Regularization

How to Reduce Overfitting With Dropout Regularization in Keras

The problem that the Machine Learning Specialist is facing is likely due to concept drift, which is a phenomenon where the statistical properties of the target variable change over time, making the model less accurate and relevant. Concept drift can occur due to various reasons, such as changes in customer preferences, market trends, product inventory, seasonality, etc. In this case, the product recommendations model may have become outdated as the product inventory changed over time, making the recommendations less appealing to the customers. To address this issue, the model should be periodically retrained using the original training data plus new data as product inventory changes. This way, the model can learn from the latest data and adapt to the changing customer behavior and preferences. Retraining the model from scratch using the original data while adding a regularization term may not be sufficient, as it does not account for the new data. Updating the model’s hyperparameters may not help either, as it does not address the underlying data distribution change. Re-engineering the model completely may not be necessary, as the model may still be valid and useful with periodic retraining.

Concept Drift - Amazon SageMaker

Detecting and Handling Concept Drift - Amazon SageMaker

Machine Learning Concepts - Amazon Machine Learning

 Initializing the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector will provide the maximum performance boost for the LSTM model. Word2vec is a technique that learns distributed representations of words based on their co-occurrence in a large corpus of text. These representations capture semantic and syntactic similarities between words, which can help the LSTM model better understand the meaning and context of the sentences in the text documents. Using word2vec embeddings that are pretrained on a relevant domain (energy sector) can further improve the performance by reducing the vocabulary mismatch and increasing the coverage of the words in the text documents. References:

AWS Machine Learning Specialty Exam Guide

AWS Machine Learning Training - Text Classification with TF-IDF, LSTM, BERT: a comparison of performance

AWS Machine Learning Training - Machine Learning - Exam Preparation Path

Word embeddings are a type of dense representation of words, which encode semantic meaning in a vector form. These embeddings are typically pre-trained on a large corpus of text data, such as a large set of books, news articles, or web pages, and capture the context in which words are used. Word embeddings can be used as features for a nearest neighbor model, which can be used to find words used in similar contexts. Downloading pre-trained word embeddings is a good way to get started quickly and leverage the strengths of these representations, which have been optimized on a large amount of data. This is likely to result in more accurate and reliable features than other options like one-hot encoding, edit distance, or using Amazon Mechanical Turk to produce synonyms.

 To find the most discussed topics in the comments that are in either English or Spanish, the machine learning specialist needs to perform two steps: first, translate the comments from Spanish to English if necessary, and second, apply a topic modeling algorithm to the comments. The following options are valid ways to accomplish these steps using AWS services:

Option C: Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Comprehend topic modeling to find the topics. Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. Amazon Comprehend topic modeling is a feature that automatically organizes a collection of text documents into topics that contain commonly used words and phrases.

Option E: Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon SageMaker Neural Topic Model (NTM) to find the topics. Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. Amazon SageMaker Neural Topic Model (NTM) is an unsupervised learning algorithm that is used to organize a corpus of documents into topics that contain word groupings based on their statistical distribution.

The other options are not valid because:

Option A: Amazon SageMaker BlazingText algorithm is not a topic modeling algorithm, but a text classification and word embedding algorithm. It cannot find the topics independently from language, as different languages have different word distributions and semantics.

Option B: Amazon SageMaker seq2seq algorithm is not a translation algorithm, but a sequence-to-sequence learning algorithm that can be used for tasks such as summarization, chatbot, and question answering. Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm is a topic modeling algorithm, but it requires the input documents to be in the same language and preprocessed into a bag-of-words format.

Option D: Amazon Lex is not a topic modeling algorithm, but a service for building conversational interfaces into any application using voice and text. It cannot extract topics from the content, but only intents and slots based on a predefined bot configuration. References:

Amazon Translate

Amazon Comprehend

Amazon SageMaker

Amazon SageMaker Neural Topic Model (NTM) Algorithm

Amazon SageMaker BlazingText

Amazon SageMaker Seq2Seq

Amazon SageMaker Latent Dirichlet Allocation (LDA) Algorithm

Amazon Lex

Applying L1 regularization encourages sparsity by penalizing weights directly, often driving many weights to zero. In this case, the ML specialist observes that all weights become zero, which suggests that the L1 regularization parameter is set too high. This high value overly penalizes non-zero weights, effectively removing all features from the model.

To improve the model, the ML specialist should reduce the L1 regularization parameter, allowing some features to retain non-zero weights. This adjustment will make the model less prone to excessive sparsity, allowing it to better capture essential patterns in the data without dropping all features. Introducing L2 regularization is another approach but may not directly resolve this specific issue of all-zero weights as effectively as reducing L1.

Amazon Fraud Detector is a managed service designed to detect potentially fraudulent online activities, such as transactions. It uses machine learning and business rules to classify activities as fraudulent or legitimate, minimizing the need for custom model training. By using the Amazon Fraud Detector prediction API, the company can automatically approve or reject transactions flagged as fraudulent, implementing an efficient risk mitigation strategy without extensive operational effort.

This approach requires minimal setup and effectively allows the company to block fraudulent transactions with high confidence, addressing the business's need to balance risk mitigation and customer impact.

The solution that will meet the requirements with the least amount of customization to transform and store the ingested data is to use Amazon Kinesis Data Analytics to read and aggregate the data hourly, transform the data and store it in Amazon S3 by using Amazon Kinesis Data Firehose. This solution leverages the built-in features of Kinesis Data Analytics to perform SQL queries on streaming data and generate hourly summaries. Kinesis Data Analytics can also output the transformed data to Kinesis Data Firehose, which can then deliver the data to S3 in a specified format and partitioning scheme. This solution does not require any custom code or additional infrastructure to process the data. The other solutions either require more customization (such as using Lambda or EMR) or do not meet the requirement of aggregating the data hourly (such as using Lambda to read the data from Kinesis Data Streams). References:

1: Boosting Resiliency with an ML-based Telemetry Analytics Architecture | AWS Architecture Blog

2: AWS Cloud Data Ingestion Patterns and Practices

3: IoT ingestion and Machine Learning analytics pipeline with AWS IoT …

4: AWS IoT Data Ingestion Simplified 101: The Complete Guide - Hevo Data

 XGBoost is a built-in Amazon SageMaker machine learning algorithm that should be used for modeling the credit card fraud detection problem. XGBoost is an algorithm that implements a scalable and distributed gradient boosting framework, which is a popular and effective technique for supervised learning problems. Gradient boosting is a method of combining multiple weak learners, such as decision trees, into a strong learner, by iteratively fitting new models to the residual errors of the previous models and adding them to the ensemble. XGBoost can handle various types of data, such as numerical, categorical, or text, and can perform both regression and classification tasks. XGBoost also supports various features and optimizations, such as regularization, missing value handling, parallelization, and cross-validation, that can improve the performance and efficiency of the algorithm.

XGBoost is suitable for the credit card fraud detection problem for the following reasons:

The problem is a binary classification problem, where the goal is to predict whether a transaction is fraudulent or not, based on the information from new transactions. XGBoost can perform binary classification by using a logistic regression objective function and outputting the probability of the positive class (fraudulent) for each transaction.

The problem involves a large and imbalanced dataset of historical data labeled as fraudulent. XGBoost can handle large-scale and imbalanced data by using distributed and parallel computing, as well as techniques such as weighted sampling, class weighting, or stratified sampling, to balance the classes and reduce the bias towards the majority class (non-fraudulent).

The problem requires a high accuracy and precision for detecting fraudulent transactions, as well as a low false positive rate for avoiding false alarms. XGBoost can achieve high accuracy and precision by using gradient boosting, which can learn complex and non-linear patterns from the data and reduce the variance and overfitting of the model. XGBoost can also achieve a low false positive rate by using regularization, which can reduce the complexity and noise of the model and prevent it from fitting spurious signals in the data.

The other options are not as suitable as XGBoost for the credit card fraud detection problem for the following reasons:

Seq2seq: Seq2seq is an algorithm that implements a sequence-to-sequence model, which is a type of neural network model that can map an input sequence to an output sequence. Seq2seq is mainly used for natural language processing tasks, such as machine translation, text summarization, or dialogue generation. Seq2seq is not suitable for the credit card fraud detection problem, because the problem is not a sequence-to-sequence task, but a binary classification task. The input and output of the problem are not sequences of words or tokens, but vectors of features and labels.

K-means: K-means is an algorithm that implements a clustering technique, which is a type of unsupervised learning method that can group similar data points into clusters. K-means is mainly used for exploratory data analysis, dimensionality reduction, or anomaly detection. K-means is not suitable for the credit card fraud detection problem, because the problem is not a clustering task, but a classification task. The problem requires using the labeled data to train a model that can predict the labels of new data, not finding the optimal number of clusters or the cluster memberships of the data.

Random Cut Forest (RCF): RCF is an algorithm that implements an anomaly detection technique, which is a type of unsupervised learning method that can identify data points that deviate from the normal behavior or distribution of the data. RCF is mainly used for detecting outliers, frauds, or faults in the data. RCF is not suitable for the credit card fraud detection problem, because the problem is not an anomaly detection task, but a classification task. The problem requires using the labeled data to train a model that can predict the labels of new data, not finding the anomaly scores or the anomalous data points in the data.

XGBoost Algorithm

Use XGBoost for Binary Classification with Amazon SageMaker

Seq2seq Algorithm

K-means Algorithm

[Random Cut Forest Algorithm]

The problem described in the question is a case of underfitting, where the neural network model performs poorly on both the training and validation sets. This means that the model has not learned the features of the data well enough and has high bias. To solve this issue, the machine learning specialist should consider the following change:

Add more data to the training set and retrain the model using transfer learning to reduce the bias: Adding more data to the training set can help the model learn more patterns and variations in the data and improve its performance. Transfer learning can also help the model leverage the knowledge from the pre-trained network and adapt it to the new data. This can reduce the bias and increase the accuracy of the model.

Transfer learning for TensorFlow image classification models in Amazon SageMaker

Transfer learning for custom labels using a TensorFlow container and “bring your own algorithm” in Amazon SageMaker

Machine Learning Concepts - AWS Training and Certification

 Transfer learning is a technique that allows us to use a model trained for a certain task as a starting point for a machine learning model for a different task. For image classification, a common practice is to use a pre-trained model that was trained on a large and general dataset, such as ImageNet, and then customize it for the specific task. One way to customize the model is to replace the last fully connected layer, which is responsible for the final classification, with a new layer that has the same number of units as the number of classes in the new task. This way, the model can leverage the features learned by the previous layers, which are generic and useful for many image recognition tasks, and learn to map them to the new classes. The new layer can be initialized with random weights, and the rest of the model can be initialized with the pre-trained weights. This method is also known as feature extraction, as it extracts meaningful features from the pre-trained model and uses them for the new task. References:

Transfer learning and fine-tuning

Deep transfer learning for image classification: a survey

Principal Component Analysis (PCA) is a dimensionality reduction technique that captures the variance within the feature space, helping to understand the directions in which data varies most. In SageMaker Data Wrangler, the multicollinearity measurement and PCA features allow the data scientist to analyze interdependencies between predictor variables while reducing redundancy. PCA transforms correlated features into a set of uncorrelated components, helping to simplify the dataset without significant loss of information, making it ideal for refining features based on variance.

Options A and D offer methods to understand feature relevance but are less effective for managing multicollinearity and variance representation in the data.

Term frequency-inverse document frequency (TF-IDF) is a technique that assigns a weight to each word in a document based on how important it is to the meaning of the document. The term frequency (TF) measures how often a word appears in a document, while the inverse document frequency (IDF) measures how rare a word is across a collection of documents. The TF-IDF weight is the product of the TF and IDF values, and it is high for words that are frequent in a specific document but rare in the overall corpus. TF-IDF can help improve the validation accuracy of a sentiment analysis model by reducing the impact of common words that have little or no sentiment value, such as “the”, “a”, “and”, etc. Scikit-learn is a popular Python library for machine learning that provides a TF-IDF vectorizer class that can transform a collection of text documents into a matrix of TF-IDF features. By using this tool, the Data Scientist can create a more informative and discriminative feature representation for the sentiment analysis task.

TfidfVectorizer - scikit-learn

Text feature extraction - scikit-learn

TF-IDF for Beginners | by Jana Schmidt | Towards Data Science

Sentiment Analysis: Concept, Analysis and Applications | by Susan Li | Towards Data Science

To create a serverless ingestion and analytics solution for high-velocity, real-time streaming data, the Data Scientist should use the following AWS services:

AWS Glue Data Catalog: This is a managed service that acts as a central metadata repository for data assets across AWS and on-premises data sources. The Data Scientist can use AWS Glue Data Catalog to create a schema of the incoming data format, which defines the structure, format, and data types of the JSON records. The schema can be used by other AWS services to understand and process the data1.

Amazon Kinesis Data Firehose: This is a fully managed service that delivers real-time streaming data to destinations such as Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and Splunk. The Data Scientist can use Amazon Kinesis Data Firehose to stream the data from the source and transform the data to a query-optimized, columnar format such as Apache Parquet or ORC using the AWS Glue Data Catalog before delivering to Amazon S3. This enables efficient compression, partitioning, and fast analytics on the data2.

Amazon S3: This is an object storage service that offers high durability, availability, and scalability. The Data Scientist can use Amazon S3 as the output datastore for the transformed data, which can be organized into buckets and prefixes according to the desired partitioning scheme. Amazon S3 also integrates with other AWS services such as Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum for analytics3.

Amazon Athena: This is a serverless interactive query service that allows users to analyze data in Amazon S3 using standard SQL. The Data Scientist can use Amazon Athena to run SQL queries against the data in Amazon S3 and connect to existing business intelligence dashboards using the Athena Java Database Connectivity (JDBC) connector. Amazon Athena leverages the AWS Glue Data Catalog to access the schema information and supports formats such as Parquet and ORC for fast and cost-effective queries4.

1: What Is the AWS Glue Data Catalog? - AWS Glue

2: What Is Amazon Kinesis Data Firehose? - Amazon Kinesis Data Firehose

3: What Is Amazon S3? - Amazon Simple Storage Service

4: What Is Amazon Athena? - Amazon Athena

 To meet the requirements with the least operational overhead, the company should use AWS Glue crawlers, AWS Glue workflows and jobs, and Amazon Fraud Detector. AWS Glue crawlers can scan the data in Amazon S3 and identify the schema, which is then stored in the AWS Glue Data Catalog. AWS Glue workflows and jobs can perform data transformations on the data in Amazon S3 using serverless Spark or Python scripts. Amazon Fraud Detector can train a model to detect fraud using the transformed data and the company’s historical fraud labels, and then generate fraud predictions using a simple API call.

Option A is incorrect because Amazon Athena is a serverless query service that can analyze data in Amazon S3 using standard SQL, but it does not perform data transformations or fraud detection.

Option C is incorrect because Amazon Redshift is a cloud data warehouse that can store and query data using SQL, but it requires provisioning and managing clusters, which adds operational overhead. Moreover, Amazon Redshift does not provide a built-in fraud detection capability.

Option E is incorrect because Amazon Redshift ML is a feature that allows users to create, train, and deploy machine learning models using SQL commands in Amazon Redshift. However, using Amazon Redshift ML would require loading the data from Amazon S3 to Amazon Redshift, which adds complexity and cost. Also, Amazon Redshift ML does not support fraud detection as a use case.

AWS Glue Crawlers

AWS Glue Workflows and Jobs

Amazon Fraud Detector

To adapt a previously trained model to a new but related task efficiently, the best practice is to leverage transfer learning. This involves retaining the learned features from the earlier model and only retraining the final layers to accommodate the new classification categories.​

In the context of a hybrid architecture combining a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN):​

CNN Component: The CNN is responsible for extracting spatial features from video frames. Since the early layers of a CNN capture generic features like edges and textures, they are often transferable across tasks. Therefore, only the last fully connected layer, which maps these features to specific object classes, needs to be reinitialized and retrained for the new set of objects.​

RNN Component: The RNN handles the temporal dynamics of the sequence data. Similar to the CNN, the earlier layers of the RNN capture general sequence patterns. Thus, reinitializing and retraining only the last layer of the RNN allows the model to adapt to the new prediction task without the need to retrain the entire network.​

This approach minimizes training time and computational resources while effectively adapting the model to new tasks.

 Amazon Comprehend is a natural language processing (NLP) service that can analyze text and extract insights such as sentiment, entities, topics, and language. Amazon Comprehend also provides custom classification and custom entity recognition features that allow users to train their own models using their own data and labels. For the scenario of routing customer claims to different queues based on categories, Amazon Comprehend custom classification is a suitable solution. The custom classifier can be trained using a .csv file that contains the claim text and the claim label as columns. The custom classifier can then be used to process incoming claims and predict the labels using the Amazon Comprehend API. The predicted labels can be used to route the claims to the appropriate queue. This solution does not require any machine learning expertise or model deployment, and it can be easily integrated with the existing application.

The other options are not suitable because:

Option A: Amazon SageMaker Object2Vec is an algorithm that can learn embeddings of objects such as words, sentences, or documents. It can be used for tasks such as text classification, sentiment analysis, or recommendation systems. However, using this algorithm requires machine learning expertise and model deployment using SageMaker, which are not available for the company.

Option B: Amazon SageMaker Latent Dirichlet Allocation (LDA) is an algorithm that can discover the topics or themes in a collection of documents. It can be used for tasks such as topic modeling, document clustering, or text summarization. However, using this algorithm requires machine learning expertise and model deployment using SageMaker, which are not available for the company. Moreover, LDA does not provide labels for the topics, but rather a distribution of words for each topic, which may not match the existing categories of the claims.

Option C: Amazon Textract is a service that can extract text and data from scanned documents or images. It can be used for tasks such as document analysis, data extraction, or form processing. However, using this service is unnecessary and inefficient for the scenario, since the company already has the claim text and label in a database. Moreover, Amazon Textract does not provide custom classification features, so it cannot be used to train a custom classifier using the existing data and labels.

Amazon Comprehend Custom Classification

Amazon SageMaker Object2Vec

Amazon SageMaker Latent Dirichlet Allocation

Amazon Textract