PMLE Practice Question: A company uses Vertex AI Prediction with a custom…

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?

⚠ Common exam trap

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.

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

✓

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.

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.
✗
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.

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PMLE Practice Question: A company uses Vertex AI Prediction with a custom…

Why each option matters

Option-by-option breakdown

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