Databricks Machine Learning Professional exam dumps

Databricks Machine Learning Professional practice question 53 of 280

Databricks Certified Machine Learning Professional. Professional level, Databricks. Free question with the correct answer and a full explanation.

Databricks Machine Learning Professional Question 53

Select 2

You are managing a machine learning project on Databricks, and your team has deployed a model to production using MLflow. After deployment, a business stakeholder requests a detailed report on the model’s performance and lineage to ensure compliance with regulatory standards. Which steps should you take to provide this information?

  1. A

    Use the MLflow Tracking UI to retrieve the model's parameters, metrics, and artifacts.

  2. B

    Export the model’s feature importance plot directly from the Databricks Model Registry.

  3. C

    Access the MLflow Model Registry to retrieve the model version and lineage details.

  4. D

    Use the Databricks AutoML feature to automatically generate the compliance report.

  5. E

    Query the model’s serving endpoint logs to retrieve performance metrics.

Show answer and explanation

Correct answers: A, C

Explanation

To provide the requested compliance report, you need to gather information about the model's parameters, metrics, artifacts, and lineage. The MLflow Tracking UI and Model Registry are the appropriate tools in Databricks for accessing this information. These tools ensure you can report on the model's training process, versioning, and deployment history, which are key aspects of regulatory compliance.

  • A. Correct.

    The MLflow Tracking UI provides detailed information about the model training process, including parameters, metrics, and artifacts, which are essential for compliance reporting.

  • B. Incorrect.

    Feature importance plots are not directly available in the Model Registry. This step is not relevant for generating a compliance report.

  • C. Correct.

    The MLflow Model Registry stores the version history, lineage, and stage transitions of a model, which are crucial for tracking compliance and audit trails.

  • D. Incorrect.

    Databricks AutoML is used for automating model training and evaluation, not for generating compliance reports.

  • E. Incorrect.

    While serving endpoint logs can be useful for monitoring live models, they do not provide the detailed lineage or training information required for compliance.

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