- A
The new model artifact is temporarily unavailable, so the endpoint falls back to the previous version.
Why wrong: Vertex AI endpoints do not fall back to older versions; they only serve the version they are configured to serve.
- B
The prediction cache is returning cached results from the old model.
Why wrong: Caching affects predictions, not the model version served; the endpoint still selects the model.
- C
The pipeline failed to update the endpoint with the new model ID.
Why wrong: If the update failed, the old model would be served permanently, not just for a few minutes.
- D
The endpoint is configured with a canary traffic split, and the old model is still receiving a fraction of traffic during the rollout.
Canary deployments gradually shift traffic, so some requests hit the old model until the rollout is complete.
Quick Answer
The answer is that the endpoint is configured with a canary traffic split, which gradually shifts requests from the old model to the new one over a defined rollout window. This is the most likely cause because Vertex AI endpoints support a gradual rollout strategy where a small percentage of traffic continues to be routed to the previous model version until the canary deployment fully completes. On the Google Professional Machine Learning Engineer exam, this concept tests your understanding of how Vertex AI manages model serving reliability and zero-downtime deployments. A common trap is assuming the new model immediately replaces the old one, but the canary split intentionally preserves a fraction of traffic to the prior version for monitoring and rollback safety. Memory tip: think of a canary in a coal mine — the old model stays alive as a safety net until the new model proves stable.
PMLE Monitoring ML solutions Practice Question
This PMLE practice question tests your understanding of monitoring ml solutions. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 company implements an ML pipeline using Vertex AI Pipelines. The pipeline trains a model using custom training jobs and then deploys it to an endpoint. The team notices that the endpoint occasionally serves an older model version for a few minutes after a new pipeline run completes. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
The endpoint is configured with a canary traffic split, and the old model is still receiving a fraction of traffic during the rollout.
D is correct because Vertex AI endpoints can be configured with a canary (gradual) traffic rollout strategy. When a new model is deployed, traffic is shifted incrementally from the old model to the new one over a specified duration. During this rollout window, the old model continues to serve a fraction of requests, which explains why users occasionally see the older model version for a few minutes after the pipeline completes.
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.
- ✗
The new model artifact is temporarily unavailable, so the endpoint falls back to the previous version.
Why it's wrong here
Vertex AI endpoints do not fall back to older versions; they only serve the version they are configured to serve.
- ✗
The prediction cache is returning cached results from the old model.
Why it's wrong here
Caching affects predictions, not the model version served; the endpoint still selects the model.
- ✗
The pipeline failed to update the endpoint with the new model ID.
Why it's wrong here
If the update failed, the old model would be served permanently, not just for a few minutes.
- ✓
The endpoint is configured with a canary traffic split, and the old model is still receiving a fraction of traffic during the rollout.
Why this is correct
Canary deployments gradually shift traffic, so some requests hit the old model until the rollout is complete.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse a canary rollout with a deployment failure or caching issue, assuming the old model persists due to an error rather than recognizing it as an intentional traffic-splitting mechanism during a gradual rollout.
Detailed technical explanation
How to think about this question
Vertex AI endpoints support traffic splitting using the `traffic_split` parameter, which maps model versions to percentages of traffic (e.g., `{"model_v1": 0.9, "model_v2": 0.1}`). During a canary rollout, the pipeline updates this split incrementally over a defined `canary_time` (default 60 seconds), causing a gradual transition. This behavior is distinct from a simple deployment, where the endpoint immediately serves the new model at 100% traffic.
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 PMLE question test?
Monitoring ML solutions — This question tests Monitoring ML solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The endpoint is configured with a canary traffic split, and the old model is still receiving a fraction of traffic during the rollout. — D is correct because Vertex AI endpoints can be configured with a canary (gradual) traffic rollout strategy. When a new model is deployed, traffic is shifted incrementally from the old model to the new one over a specified duration. During this rollout window, the old model continues to serve a fraction of requests, which explains why users occasionally see the older model version for a few minutes after the pipeline completes.
What should I do if I get this PMLE 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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
This PMLE 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 PMLE exam.
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