PDE Operationalizing machine learning models Practice Question
This PDE practice question tests your understanding of operationalizing machine learning models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data scientist has iterated on a model and produced a new version. The organization requires the ability to roll back to the previous version quickly if the new version performs poorly in production. Which approach should be used?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Upload both versions to Vertex AI Model Registry and use endpoint traffic splitting to route 100% to the safe version if needed.
Vertex AI Model Registry allows you to deploy multiple model versions and use endpoint traffic splitting to gradually shift traffic or instantly route 100% to a specific version. This enables immediate rollback by setting the traffic split to 100% for the previous model version without redeploying or changing infrastructure.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 each model version in a separate Cloud Storage bucket.
Why it's wrong here
This does not provide a deployment mechanism for quick rollback.
✗
Keep the previous model in a container image and redeploy via Cloud Run.
Why it's wrong here
This is slower and does not leverage Vertex AI's built-in version management.
✗
Use Cloud Source Repositories to tag model versions.
Why it's wrong here
Source repositories are for code, not model artifacts and deployment.
✓
Upload both versions to Vertex AI Model Registry and use endpoint traffic splitting to route 100% to the safe version if needed.
Why this is correct
The registry keeps versions; endpoint traffic allows instant switch.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that version control tools (like Cloud Source Repositories) or storage buckets are sufficient for rollback, when in fact the key requirement is a managed model registry with traffic splitting capabilities for instant, no-downtime rollback.
Detailed technical explanation
How to think about this question
Vertex AI endpoint traffic splitting works by assigning a percentage of inference requests to each deployed model version based on a weighted random selection at the load balancer level. This allows for canary deployments, A/B testing, and instant rollback by adjusting the traffic split in real time via the API or console, with no downtime. In practice, you can set the previous model's traffic to 100% within seconds if the new version shows performance degradation.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Upload both versions to Vertex AI Model Registry and use endpoint traffic splitting to route 100% to the safe version if needed. — Vertex AI Model Registry allows you to deploy multiple model versions and use endpoint traffic splitting to gradually shift traffic or instantly route 100% to a specific version. This enables immediate rollback by setting the traffic split to 100% for the previous model version without redeploying or changing infrastructure.
What should I do if I get this PDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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