- A
Always keep all historical versions deployed to enable fast rollback.
Why wrong: Keeping too many versions wastes resources; only keep necessary candidates for rollback.
- B
If two versions share the same endpoint, they must have exactly the same machine type.
Why wrong: Machine type can be different per deployed version on the same endpoint.
- C
Use traffic splitting to gradually shift traffic to a new version while monitoring performance.
Traffic splitting enables canary deployments and safe rollback.
- D
Upload each model version as a new model resource and deploy to a separate endpoint for isolation.
Using separate model resources allows independent lifecycle management and easy rollback.
- E
Use the same endpoint for multiple versions and adjust min_replica_count, max_replica_count for each version.
Why wrong: Replica counts are set per deployment, not per version individually on the same endpoint; you can use traffic split.
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.
Which TWO best practices should be followed when managing multiple model versions on Vertex AI Endpoints for a production system?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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 traffic splitting to gradually shift traffic to a new version while monitoring performance.
Option C is correct because Vertex AI Endpoints support traffic splitting, allowing you to route a percentage of inference requests to a new model version while the rest goes to the existing version. This enables gradual rollout, monitoring of performance metrics (e.g., latency, error rate), and safe rollback without downtime. It is a best practice for production systems to validate a new version before fully cutting over.
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.
- ✗
Always keep all historical versions deployed to enable fast rollback.
Why it's wrong here
Keeping too many versions wastes resources; only keep necessary candidates for rollback.
- ✗
If two versions share the same endpoint, they must have exactly the same machine type.
Why it's wrong here
Machine type can be different per deployed version on the same endpoint.
- ✓
Use traffic splitting to gradually shift traffic to a new version while monitoring performance.
Why this is correct
Traffic splitting enables canary deployments and safe rollback.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Upload each model version as a new model resource and deploy to a separate endpoint for isolation.
Why this is correct
Using separate model resources allows independent lifecycle management and easy rollback.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use the same endpoint for multiple versions and adjust min_replica_count, max_replica_count for each version.
Why it's wrong here
Replica counts are set per deployment, not per version individually on the same endpoint; you can use traffic split.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that multiple versions on the same endpoint must share identical infrastructure settings (like machine type), but Vertex AI allows heterogeneous configurations per version, and traffic splitting is the correct method for gradual rollouts, not keeping all versions or adjusting autoscaling parameters.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI Endpoints use a load balancer that distributes requests based on the traffic split percentages you set (e.g., 90% to version A, 10% to version B). This is implemented via the `traffic_split` parameter in the `deployModel` API call, which maps model version IDs to integer percentages summing to 100. A real-world scenario is A/B testing a new model with lower latency requirements; you can deploy it with a cheaper machine type and route 5% of traffic to compare performance before full rollout.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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.
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FAQ
Questions learners often ask
What does this PDE question test?
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: Use traffic splitting to gradually shift traffic to a new version while monitoring performance. — Option C is correct because Vertex AI Endpoints support traffic splitting, allowing you to route a percentage of inference requests to a new model version while the rest goes to the existing version. This enables gradual rollout, monitoring of performance metrics (e.g., latency, error rate), and safe rollback without downtime. It is a best practice for production systems to validate a new version before fully cutting over.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.
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