A team is required to maintain an audit trail of model changes, including who promoted a model to production and when. They also need to ensure that only approved models are deployed. Which combination of Databricks features should they use?

Model Registry with stage transition permissions and audit logs.

Delta Lake time travel to track model versions.

MLflow Tracking to log experiments and Delta Lake to store training data.

Databricks Jobs to schedule model training and deployment.

A team needs to enforce that only approved models can be deployed to production. They use MLflow Model Registry. Which feature should they enable to ensure that only models with the 'Production' stage are used in production serving?

Delta Lake time travel to track model versions.

Model version stage transitions with approval permissions.

Model registry webhooks to notify the team of changes.

MLflow experiment tracking to compare model metrics.

DatabricksCertified Machine Learning Professional

Domain 2Objective 1

Model Lifecycle Management MACHINE-LEARNING-PROFESSIONAL Practice Questions (Page 2)

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 17 practice questions to prepare you well beyond it. (estimate)

17questions here

4free pages

3concepts

44%of the exam

Questions 6–10

6foundation · easy
Which Databricks feature is primarily used for model versioning and stage transitions to support governance in the model lifecycle?
Select an answer first
7expert · medium
A team is required to maintain an audit trail of model changes, including who promoted a model to production and when. They also need to ensure that only approved models are deployed. Which combination of Databricks features should they use?
Select an answer first
8foundation · easy
In a model lifecycle pipeline, which stage is primarily responsible for validating model performance against acceptance criteria before the model is promoted to serve live traffic?
Select an answer first
9application · medium
A team needs to enforce that only approved models can be deployed to production. They use MLflow Model Registry. Which feature should they enable to ensure that only models with the 'Production' stage are used in production serving?
Select an answer first
10application · medium
A data science team is developing a model and wants to track all experiments, including parameters, metrics, and artifacts. They also want to compare different runs to select the best model. Which Databricks feature should they use?
Select an answer first

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Model Lifecycle Management MACHINE-LEARNING-PROFESSIONAL Practice Questions (Page 2)

Questions 6–10

Which Databricks feature is primarily used for model versioning and stage transitions to support governance in the model lifecycle?

A team is required to maintain an audit trail of model changes, including who promoted a model to production and when. They also need to ensure that only approved models are deployed. Which combination of Databricks features should they use?

In a model lifecycle pipeline, which stage is primarily responsible for validating model performance against acceptance criteria before the model is promoted to serve live traffic?

A team needs to enforce that only approved models can be deployed to production. They use MLflow Model Registry. Which feature should they enable to ensure that only models with the 'Production' stage are used in production serving?

A data science team is developing a model and wants to track all experiments, including parameters, metrics, and artifacts. They also want to compare different runs to select the best model. Which Databricks feature should they use?

Keep studying — ML Ops