Databricks Machine Learning Professional exam dumps

Databricks Machine Learning Professional practice question 105 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 105

Single answer

A machine learning team at your organization has deployed a model to the 'Staging' stage in the MLflow Model Registry. They want to automatically trigger a Databricks Job to validate the model whenever it transitions to the 'Production' stage. How can you configure this workflow?

  1. A

    Create a Databricks Job and set the model transition event trigger to execute the Job whenever the model transitions to the 'Production' stage.

  2. B

    Use the Databricks REST API to poll the MLflow Model Registry for stage transitions and manually trigger the Job when the model reaches the 'Production' stage.

  3. C

    Set up an MLflow Webhook that listens for model transition events to the 'Production' stage and triggers the Databricks Job.

  4. D

    Manually monitor the MLflow Model Registry and trigger the Databricks Job using the UI when the model transitions to the 'Production' stage.

Show answer and explanation

Correct answer: C

Explanation

To automate the triggering of a Databricks Job when a model transitions to the 'Production' stage, the recommended approach is to use MLflow Webhooks. MLflow Webhooks can listen for specific model registry events (e.g., stage transitions) and trigger external actions, such as running a Databricks Job. This is the most efficient and scalable solution compared to manual monitoring or polling via the API.

  • A. Incorrect.

    Incorrect: Databricks Jobs do not have built-in triggers for MLflow model transition events. You need another mechanism, such as MLflow Webhooks, to achieve this automation.

  • B. Incorrect.

    Incorrect: Polling the MLflow Model Registry using the REST API is inefficient and not ideal for triggering Jobs. It is better to use event-driven mechanisms such as Webhooks.

  • C. Correct.

    Correct: MLflow Webhooks can listen for model transition events, such as transitioning to the 'Production' stage, and trigger external actions like Databricks Jobs.

  • D. Incorrect.

    Incorrect: Manually monitoring the MLflow Model Registry is not automated and does not meet the requirement of triggering the Job automatically.

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