The answer is that traffic is not split to the new model version. In Vertex AI Prediction, deploying a new model version to an existing endpoint does not automatically route any requests to it; the platform defaults the new version to 0% traffic allocation, leaving the old version handling 100% of the incoming traffic. This behavior is by design to prevent accidental disruption, meaning you must explicitly configure a traffic split using the console or the `gcloud ai endpoints update` command with the `--traffic-split` flag. On the Google Professional Machine Learning Engineer exam, this concept tests your understanding of Vertex AI’s deployment lifecycle and traffic management, often appearing as a trap where candidates assume a new version automatically receives traffic. A common memory tip is to think of it like a light switch: deploying a new version turns on the bulb, but you still have to flip the traffic split switch to let the light shine through.
PMLE Serving and scaling models Practice Question
This PMLE practice question tests your understanding of serving and scaling 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 company uses Vertex AI Prediction with a custom container for a TensorFlow model. They notice that after deploying a new model version, requests still go to the old version. 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 the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Traffic is not split to the new model version
In Vertex AI Prediction, when you deploy a new model version to an existing endpoint, you must explicitly allocate traffic to it. By default, the new version receives 0% traffic, so all requests continue to be served by the old version. The correct fix is to update the endpoint's traffic split, for example via the console or the `gcloud ai endpoints update` command with the `--traffic-split` flag.
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 custom container is not compatible with Vertex AI
Why it's wrong here
Vertex AI supports custom containers.
✗
The model is cached and needs cache invalidation
Why it's wrong here
Caching may affect but not the primary cause.
✓
Traffic is not split to the new model version
Why this is correct
Traffic splitting must be adjusted to route to the new version.
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.
✗
The new model version was not deployed to the same endpoint
Why it's wrong here
If deployed to same endpoint, traffic splitting controls routing.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that deploying a new model version automatically replaces the old one, when in fact Vertex AI requires an explicit traffic split update to shift requests to the new version.
Detailed technical explanation
How to think about this question
Vertex AI endpoints use a traffic split mechanism based on percentage values that sum to 100%. When you deploy a new model version, the API automatically assigns it 0% traffic unless you explicitly set a split. Under the hood, the endpoint's routing layer uses these percentages to direct incoming prediction requests to the correct model version's serving infrastructure. A common real-world scenario is deploying a canary version with 5% traffic to validate performance before rolling out to 100%.
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.
Serving and scaling models — This question tests Serving and scaling models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Traffic is not split to the new model version — In Vertex AI Prediction, when you deploy a new model version to an existing endpoint, you must explicitly allocate traffic to it. By default, the new version receives 0% traffic, so all requests continue to be served by the old version. The correct fix is to update the endpoint's traffic split, for example via the console or the `gcloud ai endpoints update` command with the `--traffic-split` flag.
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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Question Discussion
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