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1

For a customer churn use case, a classification model predicts whether a customer will churn. Which type of target is this?

A. A categorical label B. A continuous numeric value C. A timestamp index D. A storage location

2

When preparing a tabular dataset, a deployed model's input distribution begins changing. What should the team do?

A. Monitor drift and data quality, investigate impact, and define retraining or review triggers B. Assume performance cannot change after deployment C. Delete all previous metrics D. Disable operational logging

3

In a repeatable model training workflow, a feature is only available after the outcome occurs. Why should it generally be excluded from prediction features?

A. It can cause target leakage B. It guarantees higher real-world accuracy C. It removes the need for a validation set D. It automatically fixes class imbalance

4

For a production scoring application, a classification model predicts whether a customer will churn. Which type of target is this?

A. A categorical label B. A continuous numeric value C. A timestamp index D. A storage location

5

In an MLflow experiment, a deployed model's input distribution begins changing. What should the team do?

A. Monitor drift and data quality, investigate impact, and define retraining or review triggers B. Assume performance cannot change after deployment C. Delete all previous metrics D. Disable operational logging

6

For a customer churn use case, a feature is only available after the outcome occurs. Why should it generally be excluded from prediction features?

A. It can cause target leakage B. It guarantees higher real-world accuracy C. It removes the need for a validation set D. It automatically fixes class imbalance

7

When preparing a tabular dataset, a classification model predicts whether a customer will churn. Which type of target is this?

A. A categorical label B. A continuous numeric value C. A timestamp index D. A storage location

8

In a repeatable model training workflow, a deployed model's input distribution begins changing. What should the team do?

A. Monitor drift and data quality, investigate impact, and define retraining or review triggers B. Assume performance cannot change after deployment C. Delete all previous metrics D. Disable operational logging

9

For a production scoring application, a feature is only available after the outcome occurs. Why should it generally be excluded from prediction features?

A. It can cause target leakage B. It guarantees higher real-world accuracy C. It removes the need for a validation set D. It automatically fixes class imbalance

10

In an MLflow experiment, a classification model predicts whether a customer will churn. Which type of target is this?

A. A categorical label B. A continuous numeric value C. A timestamp index D. A storage location

Databricks Certified Machine Learning Associate

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Course Questions

For a customer churn use case, a classification model predicts whether a customer will churn. Which type of target is this?

When preparing a tabular dataset, a deployed model's input distribution begins changing. What should the team do?

In a repeatable model training workflow, a feature is only available after the outcome occurs. Why should it generally be excluded from prediction features?

For a production scoring application, a classification model predicts whether a customer will churn. Which type of target is this?

In an MLflow experiment, a deployed model's input distribution begins changing. What should the team do?

For a customer churn use case, a feature is only available after the outcome occurs. Why should it generally be excluded from prediction features?

When preparing a tabular dataset, a classification model predicts whether a customer will churn. Which type of target is this?

In a repeatable model training workflow, a deployed model's input distribution begins changing. What should the team do?

For a production scoring application, a feature is only available after the outcome occurs. Why should it generally be excluded from prediction features?

In an MLflow experiment, a classification model predicts whether a customer will churn. Which type of target is this?

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