What is the recommended way to update a model serving endpoint that was deployed using a Databricks Asset Bundle?

Modify the bundle configuration and redeploy using the Databricks CLI.

Manually edit the endpoint in the Databricks UI.

Delete the endpoint and recreate it from scratch.

Use the MLflow API to change the endpoint directly.

An ML team uses Databricks Asset Bundles to manage registered models. They want to promote a model from 'Staging' to 'Production' after validation. What is the recommended way to handle this stage transition in DABs?

Manually transition the model stage in the MLflow UI and then update the bundle file to reflect the new stage.

Create a new registered model for the production version and update the serving endpoint to point to it.

Use the MLflow API to transition the stage and then run `databricks bundle deploy` to sync the state.

Update the model version's stage in the bundle configuration and redeploy the bundle.

How can you promote a model version from Staging to Production in a Databricks Asset Bundle?

Use the MLflow UI to manually change the stage.

Change the 'stage' field in the bundle configuration and redeploy.

Delete the Staging version and recreate it as Production.

Create a new model with the same name and set it to Production.

Which cluster configuration is most appropriate for a machine learning training job that runs nightly and has predictable resource needs?

A cluster with a fixed size and no autoscaling, left running continuously.

A job cluster that is created for the job and terminated after completion.

A high-concurrency cluster with shared access for multiple users.

An interactive cluster with autoscaling enabled.

When configuring a model serving endpoint in DABs, how can you pass environment variables to the serving environment?

Using the 'environment_variables' field in the endpoint configuration.

By using the 'env' field in the model version.

By including them in the model's MLflow artifact.

By setting them in the cluster configuration.

DatabricksCertified Machine Learning Professional

Domain 2Objective 3

Environment Architectures MACHINE-LEARNING-PROFESSIONAL Practice Questions (Page 4)

Part of the ML Ops domain, which accounts for 44% of the MACHINE-LEARNING-PROFESSIONAL exam. Databricks does not publish an official question count, but from its 120-minute exam (~50–80 total, ~22–35 in this domain), expect 4–7 from this objective — we provide 28 practice questions to prepare you well beyond it. (estimate)

28questions here

6free pages

9concepts

44%of the exam

Questions 16–20

16foundation · easy
What is the recommended way to update a model serving endpoint that was deployed using a Databricks Asset Bundle?
Select an answer first
17application · medium
An ML team uses Databricks Asset Bundles to manage registered models. They want to promote a model from 'Staging' to 'Production' after validation. What is the recommended way to handle this stage transition in DABs?
Select an answer first
18foundation · easy
How can you promote a model version from Staging to Production in a Databricks Asset Bundle?
Select an answer first
19foundation · easy
Which cluster configuration is most appropriate for a machine learning training job that runs nightly and has predictable resource needs?
Select an answer first
20foundation · easy
When configuring a model serving endpoint in DABs, how can you pass environment variables to the serving environment?
Select an answer first

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