A team is developing an ML pipeline in Databricks. They have a dev environment, a test environment, and a production environment. They want to validate that the pipeline works end-to-end with real data before deploying to production. Which type of testing should they perform in the test environment?

Load testing to simulate production traffic

Unit testing of individual functions

Smoke testing of the deployment only

Integration testing of the full pipeline

A company is deploying a new ML model to production. They have a staging environment that mirrors production. Which testing type is most appropriate to run in staging before the production deployment?

Integration tests with production-like data

Performance testing on a small sample

Unit tests on individual functions

A/B testing against the current production model

A team is designing an integration test for their ML pipeline that runs in Databricks. The pipeline uses a feature store, trains a model, and deploys it to a serving endpoint. They want the integration test to run automatically in CI/CD. Which approach is most robust?

Use a notebook that only tests the evaluation metrics on historical data.

Deploy the model to production and monitor its performance for a week.

Run unit tests on the feature engineering code and the training code separately.

Run the pipeline in a test environment with a small dataset and verify the model endpoint returns predictions.

A team is deciding how to organize their Databricks code. They have a notebook that contains both data processing and model training functions. They want to add unit tests. What is a key benefit of moving the functions to a separate Python module?

It eliminates the need for integration testing.

It makes the functions easier to test in isolation.

It automatically improves the model's performance.

It reduces the number of notebooks in the workspace.

DatabricksCertified Machine Learning Professional

Domain 2Objective 2

Validation Testing 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

4concepts

44%of the exam

Questions 6–10

6application · medium
A team is developing an ML pipeline in Databricks. They have a dev environment, a test environment, and a production environment. They want to validate that the pipeline works end-to-end with real data before deploying to production. Which type of testing should they perform in the test environment?
Select an answer first
7application · medium
A company is deploying a new ML model to production. They have a staging environment that mirrors production. Which testing type is most appropriate to run in staging before the production deployment?
Select an answer first
8expert · hard
A team is designing an integration test for their ML pipeline that runs in Databricks. The pipeline uses a feature store, trains a model, and deploys it to a serving endpoint. They want the integration test to run automatically in CI/CD. Which approach is most robust?
Select an answer first
9application · medium
A team is deciding how to organize their Databricks code. They have a notebook that contains both data processing and model training functions. They want to add unit tests. What is a key benefit of moving the functions to a separate Python module?
Select an answer first
10foundation · easy
In which environment stage is it most appropriate to run comprehensive integration tests that validate the interaction between multiple components of an ML pipeline?
Select an answer first

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Validation Testing MACHINE-LEARNING-PROFESSIONAL Practice Questions (Page 2)

Questions 6–10

A company is deploying a new ML model to production. They have a staging environment that mirrors production. Which testing type is most appropriate to run in staging before the production deployment?

A team is designing an integration test for their ML pipeline that runs in Databricks. The pipeline uses a feature store, trains a model, and deploys it to a serving endpoint. They want the integration test to run automatically in CI/CD. Which approach is most robust?

A team is deciding how to organize their Databricks code. They have a notebook that contains both data processing and model training functions. They want to add unit tests. What is a key benefit of moving the functions to a separate Python module?

In which environment stage is it most appropriate to run comprehensive integration tests that validate the interaction between multiple components of an ML pipeline?

Keep studying — ML Ops