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Databricks Certified Machine Learning Professional
Last Updated: Oct 2, 2024
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60 Questions and Answers for the Databricks Databricks-Machine-Learning-Professional exam
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Databricks Certified Machine Learning Professional Syllabus
- Identify the requirements for tracking nested runs/ Describe an MLflow flavor and the benefits of using MLflow flavors
- Test whether the updated model performs better on the more recent data/ Identify when retraining and deploying an updated model is a probable solution to drift
- Create, overwrite, merge, and read Feature Store tables in machine learning workflows / View Delta table history and load a previous version of a Delta table
- Identify a use case for HTTP webhooks and where the Webhook URL needs to come/ Identify advantages of using Job clusters over all-purpose clusters
- Describe the advantages of using the pyfunc MLflow flavor/ Manually log parameters, models, and evaluation metrics using MLflow
- Identify live serving benefits of querying precomputed batch predictions/ Describe Structured Streaming as a common processing tool for ETL pipelines
- Describe concept drift and its impact on model efficacy/ Describe summary statistic monitoring as a simple solution for numeric feature drift
- Identify which code block will trigger a shown webhook/ Describe the basic purpose and user interactions with Model Registry
- Identify less performant data storage as a solution for other use cases/ Describe why complex business logic must be handled in streaming deployments
- Identify that data can arrive out-of-order with structured streaming/ Identify how model serving uses one all-purpose cluster for a model deployment
- Identify JIT feature values as a need for real-time deployment/ Describe how to list all webhooks and how to delete a webhook
- Describe model serving deploys and endpoint for every stage/ Identify scenarios in which feature drift and/or label drift are likely to occur