PMLE Collaborating to manage data and models Practice Question

Which TWO practices help ensure reproducible ML experiments?

⚠ Common exam trap

Google Cloud often tests the distinction between practices that improve reproducibility (like tracking parameters and versioning code) versus practices that improve cost efficiency or speed (like using preemptible VMs or temporary storage), leading candidates to conflate operational convenience with scientific reproducibility.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Use Vertex AI Experiments to track parameters and metrics

Vertex AI Experiments automatically logs parameters, metrics, and artifacts for each run, creating a complete lineage that enables exact reproduction of results. By tracking these details alongside the code version, you can recreate the exact environment and configuration that produced a given outcome, which is essential for reproducibility.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Store all artifacts in a temporary bucket
Why it's wrong here
Temporary storage risks data loss; use persistent storage.
✗
Use a random seed for each run
Why it's wrong here
A random seed reduces reproducibility; a fixed seed is better.
✓
Use Vertex AI Experiments to track parameters and metrics
Why this is correct
Experiments record the exact configuration and results.
✓
Version control training code with Cloud Source Repositories
Why this is correct
Version control ensures the exact code used can be retrieved.
✗
Use preemptible VMs
Why it's wrong here
Preemptible VMs can be terminated, affecting reproducibility.

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PMLE Collaborating to manage data and models Practice Question

Why each option matters

Option-by-option breakdown

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