A team has a production model that was trained with a specific library version and custom preprocessing code. They need to deploy a fix that changes the preprocessing logic, which affects the model's input format. The team must ensure the deployed model and the preprocessing code are always in sync. Which promotion strategy should they use?

Promote the code change through a CI/CD pipeline, retrain the model with the new preprocessing, and deploy the new model version together with the updated code.

Promote the new model version to 'Challenger' and run a shadow deployment, but do not update the preprocessing code until the model is proven.

Promote only the new model version to the 'Champion' alias in the registry, keeping the old preprocessing code in production.

Use the MLflow model registry to promote the code itself as a model artifact, bypassing the CI/CD pipeline.

Which MLflow Client API method is used to set a tag on a registered model?

MlflowClient().set_registered_model_tag()

MlflowClient().update_registered_model()

A model registry administrator needs to mark a specific model version as 'archived' by setting a tag, and later remove that tag when the model is reactivated. They are using the MLflow Client API. Which sequence of calls should they use?

`client.set_registered_model_tag(name='ml.models.churn_model', key='status', value='archived')` and later `client.delete_registered_model_tag(name='ml.models.churn_model', key='status')`

`client.update_model_version(name='ml.models.churn_model', version=3, description='archived')` and later `client.update_model_version(name='ml.models.churn_model', version=3, description='')`

`mlflow.set_tag(key='status', value='archived')` and later `mlflow.delete_tag(key='status')`

`client.set_model_version_tag(name='ml.models.churn_model', version=3, key='status', value='archived')` and later `client.delete_model_version_tag(name='ml.models.churn_model', version=3, key='status')`

In the MLflow model registry, what is the purpose of an alias?

To permanently lock a model version

To assign a mutable, semantic name to a specific model version

To store the model's training data

To replace the model version number

DatabricksCertified Machine Learning Associate

Domain 1Objective 1

MLflow and Model Lifecycle Management MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 3)

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

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Questions 11–15

11expert · hard
A team has a production model that was trained with a specific library version and custom preprocessing code. They need to deploy a fix that changes the preprocessing logic, which affects the model's input format. The team must ensure the deployed model and the preprocessing code are always in sync. Which promotion strategy should they use?
Select an answer first
12foundation · easy
Which MLflow Client API method is used to set a tag on a registered model?
Select an answer first
13application · medium
A model registry administrator needs to mark a specific model version as 'archived' by setting a tag, and later remove that tag when the model is reactivated. They are using the MLflow Client API. Which sequence of calls should they use?
Select an answer first
14foundation · easy
A data scientist wants to programmatically retrieve all MLflow runs from a specific experiment and compare their validation accuracy to find the best-performing run. Which MLflow Client API method should they use?
Select an answer first
15foundation · easy
In the MLflow model registry, what is the purpose of an alias?
Select an answer first

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MLflow and Model Lifecycle Management MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 3)

Questions 11–15

Which MLflow Client API method is used to set a tag on a registered model?

A model registry administrator needs to mark a specific model version as 'archived' by setting a tag, and later remove that tag when the model is reactivated. They are using the MLflow Client API. Which sequence of calls should they use?

A data scientist wants to programmatically retrieve all MLflow runs from a specific experiment and compare their validation accuracy to find the best-performing run. Which MLflow Client API method should they use?

In the MLflow model registry, what is the purpose of an alias?

Keep studying — Section 1: Databricks Machine Learning