AI-300 Certification Practice Question #15

Question

A team runs training jobs by using multiple Azure Machine Learning pipelines.
The team must ensure that all runs use the same Python packages and system libraries. The solution must allow dependency updates to be versioned without modifying training code.
You need to configure the workspace so that runtime dependencies are consistent and reusable.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Accepted Answer

The Azure ML environment lifecycle is **define → register → reference → re-version**: author the dependency spec, register it as a versioned workspace asset, reference it from jobs/pipelines so every run uses the same packages, and create a new environment version whenever dependencies change. This versions dependencies *without modifying training code* — exactly the requirement. "Modify scripts to install packages at runtime" defeats reproducibility, and a loose "configuration file" isn't the AML mechanism, so both are distractors. uncledana (5 votes, Highly Voted) and 03f65a8 (3 votes) agree.

More AI-300 practice questions