Databricks Machine Learning Professional exam dumps

Databricks Machine Learning Professional practice question 72 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 72

Select 2

A data science team has trained multiple models for a predictive maintenance use case and wants to use Databricks Model Registry to manage these models effectively. Which of the following actions can be performed using the Model Registry?

  1. A

    Track the lineage of experiments and datasets used to train the models.

  2. B

    Transition a model version to different stages such as 'Staging' or 'Production'.

  3. C

    Serve a registered model directly as an endpoint without deploying to a production environment.

  4. D

    Archive outdated model versions to prevent their usage in production workflows.

  5. E

    Compare the performance metrics of different registered models side-by-side.

Show answer and explanation

Correct answers: B, D

Explanation

Databricks Model Registry is designed to manage the lifecycle of machine learning models. It allows users to transition model versions between stages, archive models, and maintain a centralized repository for model management. However, it does not directly handle experiment lineage, serve models as endpoints, or provide tools for side-by-side comparison of metrics.

  • A. Incorrect.

    The Model Registry itself does not track the lineage of experiments or datasets; this is handled by MLflow's experiment tracking functionality.

  • B. Correct.

    The Model Registry allows users to transition model versions between stages like 'Staging', 'Production', and 'Archived', making it easier to manage the lifecycle of a model.

  • C. Incorrect.

    While registered models can be deployed to production, the Model Registry does not serve models directly as endpoints. Deployment requires additional steps and infrastructure.

  • D. Correct.

    The Model Registry provides functionality to archive outdated model versions, ensuring they are not used in production workflows while still retaining them for historical reference.

  • E. Incorrect.

    The Model Registry does not natively provide a side-by-side comparison of performance metrics. Comparing metrics typically requires custom dashboards or external tools.

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