CERTIFIED-MACHINE-LEARNING-PROFESSIONAL Question #33: Real Exam Question with Answer & Explanation

Question

A machine learning engineer wants to programmatically create a new Databricks Job whose schedule depends on the result of some automated tests in a machine learning pipeline. Which of the following Databricks tools can be used to programmatically create the Job?

Options

• AMLflow APIs
• BAutoML APIs
• CMLflow Client
• DJobs cannot be created programmatically
• EDatabricks REST APIs

Explanation

Databricks REST APIs (option E) are the correct tool because they expose a /jobs/create endpoint that lets you programmatically define job configurations, schedules, clusters, and tasks via HTTP requests - making it ideal for dynamic, automated pipelines where the schedule is computed at runtime.

Why the distractors are wrong:

• A. MLflow APIs - MLflow handles experiment tracking, model registry, and run management; it has no concept of Databricks Jobs.
• B. AutoML APIs - AutoML automates model training and selection; it produces runs and experiments, not scheduled Jobs.
• C. MLflow Client - The MlflowClient is the Python interface to MLflow tracking/registry features, not to Databricks job orchestration.
• D. Jobs cannot be created programmatically - Incorrect; the REST API (and the Databricks SDK, which wraps it) fully supports job creation.

Memory tip: Think of the Databricks REST API as the "control plane" - anything you can do in the Databricks UI (create jobs, manage clusters, trigger runs) can be done via REST. MLflow and AutoML are strictly ML-experiment tools; they don't cross into job scheduling territory.

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