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Amazon Web Services MLS-C01 Exam Dumps - Actual Questions Answers

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  • Last Update: Jul 20, 2024
  • Questions: 281 questions with Expert Explanation
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MLS-C01 Questions and Answers

Question # 1

A Machine Learning Specialist is creating a new natural language processing application that processes a dataset comprised of 1 million sentences The aim is to then run Word2Vec to generate embeddings of the sentences and enable different types of predictions -

Here is an example from the dataset

"The quck BROWN FOX jumps over the lazy dog "

Which of the following are the operations the Specialist needs to perform to correctly sanitize and prepare the data in a repeatable manner? (Select THREE)

A.

Perform part-of-speech tagging and keep the action verb and the nouns only

B.

Normalize all words by making the sentence lowercase

C.

Remove stop words using an English stopword dictionary.

D.

Correct the typography on "quck" to "quick."

E.

One-hot encode all words in the sentence

F.

Tokenize the sentence into words.

Question # 2

A data scientist is developing a pipeline to ingest streaming web traffic data. The data scientist needs to implement a process to identify unusual web traffic patterns as part of the pipeline. The patterns will be used downstream for alerting and incident response. The data scientist has access to unlabeled historic data to use, if needed.

The solution needs to do the following:

  • Calculate an anomaly score for each web traffic entry.
  • Adapt unusual event identification to changing web patterns over time.

Which approach should the data scientist implement to meet these requirements?

A.

Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker Random Cut Forest (RCF) built-in model. Use an Amazon Kinesis Data Stream to process the incoming web traffic data. Attach a preprocessing AWS Lambda function to perform data enrichment by calling the RCF model to calculate the anomaly score for each record.

B.

Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker built-in XGBoost model. Use an Amazon Kinesis Data Stream to process the incoming web traffic data. Attach a preprocessing AWS Lambda function to perform data enrichment by calling the XGBoost model to calculate the anomaly score for each record.

C.

Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an input source for Amazon Kinesis Data Analytics. Write a SQL query to run in real time against the streaming data with the k-Nearest Neighbors (kNN) SQL extension to calculate anomaly scores for each record using a tumbling window.

D.

Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an input source for Amazon Kinesis Data Analytics. Write a SQL query to run in real time against the streaming data with the Amazon Random Cut Forest (RCF) SQL extension to calculate anomaly scores for each record using a sliding window.

Question # 3

IT leadership wants Jo transition a company's existing machine learning data storage environment to AWS as a temporary ad hoc solution The company currently uses a custom software process that heavily leverages SOL as a query language and exclusively stores generated csv documents for machine learning

The ideal state for the company would be a solution that allows it to continue to use the current workforce of SQL experts The solution must also support the storage of csv and JSON files, and be able to query over semi-structured data The following are high priorities for the company:

• Solution simplicity

• Fast development time

• Low cost

• High flexibility

What technologies meet the company's requirements?

A.

Amazon S3 and Amazon Athena

B.

Amazon Redshift and AWS Glue

C.

Amazon DynamoDB and DynamoDB Accelerator (DAX)

D.

Amazon RDS and Amazon ES

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