Databricks Databricks-Machine-Learning-Professional Exam Dumps - Actual Questions Answers

Real-time deployment is a paradigm that can centrally compute predictions for a single record with exceedingly fast results. Real-time deployment involves exposing a model as a REST API endpoint that can receive requests and return predictions in milliseconds. This paradigm is suitable for scenarios where low latency and high throughput are required, such as fraud detection, recommendation systems, or chatbots. The other deployment paradigms are not as fast or as centralized as real-time deployment. Streaming deployment involves processing data streams in micro-batches, which can introduce some delay. Batch deployment involves processing large batches of data at scheduled intervals, which can take hours or days. Edge or on-device deployment involves deploying models to edge devices, such as mobile phones or IoT sensors, which can reduce the network overhead but also limit the computational power and model size. https://docs.databricks.com/en/machine-learning/ml-tutorials.html

MLflow Model Serving is a feature that allows users to host MLflow models as REST endpoints on Databricks1. To query a model version that is in the Production stage of the MLflow Model Registry, the user can use the following model URI format: https:// /model-serving///invocations2. In this case, the model name is recommender and the stage is Production, so the correct model URI is https:// /model-serving/recommender/Production/invocations. The other options are incorrect because they either use the wrong prefix (model instead of model-serving), the wrong stage name (stage-production instead of Production), or require the version number of the model version in Production, which is not necessary for querying the model by stage. References: Announcing MLflow Model Serving on Databricks, [MLflow Model Serving], [MLflow Model Registry]

Label drift is a type of data drift that occurs when there is a change in the probability of a label P(Y). This means that the output distribution of the model’s data source has changed over time, which can affect the performance and accuracy of the model. For example, if a model is trained to classify images of animals, and the data source starts to provide more images of cats than dogs, this would be an example of label drift. Label drift can be caused by various factors, such as changes in user behavior, environmental conditions, market trends, etc. Label drift can be detected and handled by various methods, such as periodic retraining, online learning, ensemble methods, etc123 References:

• Monitoring Feature Drift - Databricks
• Drift Metrics: How to Select the Right Metric to Analyze Drift
• Detect data drift on datasets (preview) - Azure Machine Learning