To promote a challenger model to champion using the MLflow Client API, which method should be used?

MlflowClient().set_registered_model_alias()

MlflowClient().transition_model_version_stage()

MlflowClient().update_model_version()

A team is comparing two MLflow runs in the MLflow UI to decide which model to promote. They need to see the exact dataset version used for training, the git commit hash of the code, and the current alias assigned to each model version. Where in the MLflow UI can they find all of this information?

Open the 'Metrics' tab of each run, which automatically displays dataset versions, git commits, and model aliases.

Open the 'Parameters' tab of each run, which shows the dataset version and git commit, and then check the 'Run Details' page for aliases.

Open each run's detail page and inspect the 'Tags' section, which displays user-defined tags like `dataset_version` and `git_commit`, and then navigate to the 'Model Versions' section of the registered model to see aliases.

Open the 'Artifacts' section of each run, which stores the dataset version and git commit as files, and then check the 'Experiments' page for aliases.

A team has a production model that is performing well. They have a new challenger model that shows a 2% improvement in offline metrics, but the challenger was trained with a different feature engineering library that is not yet installed in the production environment. The team needs to decide whether to promote the model or the code. What is the best course of action?

Promote the challenger model to the 'Champion' alias immediately, since the offline metrics are better.

Keep the current champion model and reject the challenger, since the improvement is only 2%.

Promote the challenger model to 'Challenger' alias and run a shadow deployment without updating the code.

Promote the code first to install the new library, then promote the challenger model to the 'Champion' alias.

To register a model in the Unity Catalog registry using the MLflow Client API, which argument must be specified in the create_registered_model() call?

The full model name in the format 'catalog.schema.model_name'

DatabricksCertified Machine Learning Associate

Domain 1Objective 1

MLflow and Model Lifecycle Management MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 4)

Part of the Section 1: Databricks Machine Learning domain, which makes up ~19% of our current practice bank. Databricks does not publish an official question count, but from its 90-minute exam (~35–60 total, ~7–11 in this domain), expect 2–3 from this objective — we provide 19 practice questions to prepare you well beyond it. (estimate)

19questions here

4free pages

8concepts

Questions 16–19

16foundation · easy
To promote a challenger model to champion using the MLflow Client API, which method should be used?
Select an answer first
17application · medium
A team is comparing two MLflow runs in the MLflow UI to decide which model to promote. They need to see the exact dataset version used for training, the git commit hash of the code, and the current alias assigned to each model version. Where in the MLflow UI can they find all of this information?
Select an answer first
18expert · hard
A team has a production model that is performing well. They have a new challenger model that shows a 2% improvement in offline metrics, but the challenger was trained with a different feature engineering library that is not yet installed in the production environment. The team needs to decide whether to promote the model or the code. What is the best course of action?
Select an answer first
19foundation · easy
To register a model in the Unity Catalog registry using the MLflow Client API, which argument must be specified in the create_registered_model() call?
Select an answer first

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MLflow and Model Lifecycle Management MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 4)

Questions 16–19

To promote a challenger model to champion using the MLflow Client API, which method should be used?

A team is comparing two MLflow runs in the MLflow UI to decide which model to promote. They need to see the exact dataset version used for training, the git commit hash of the code, and the current alias assigned to each model version. Where in the MLflow UI can they find all of this information?

To register a model in the Unity Catalog registry using the MLflow Client API, which argument must be specified in the create_registered_model() call?

Keep studying — Section 1: Databricks Machine Learning