Quick Answer
The correct answer is to update the endpoint to deploy the new model version from the registry and adjust the traffic split. This is necessary because uploading a model to the Vertex AI Model Registry is a separate action from deploying it to an endpoint; the endpoint’s traffic split configuration explicitly controls which model version serves live requests. In this scenario, the pipeline successfully uploaded the retrained model, but the endpoint remained pinned to 100% traffic for the old version, causing stale recommendations. On the Google Professional Data Engineer exam, this question tests your understanding of the deployment lifecycle in Vertex AI, specifically that model registry updates do not automatically propagate to endpoints—a common trap where candidates assume a successful pipeline run implies automatic endpoint updates. A helpful memory tip is “Upload is not deploy; traffic split must follow”—always verify that after a new model is registered, you explicitly update the endpoint’s deployed model and adjust its traffic allocation to serve the new version.
This PDE practice question tests your understanding of operationalizing machine learning models. 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 retail company uses a Vertex AI endpoint to serve product recommendations. The model is a TensorFlow model deployed with a custom container. Recently, users have reported that recommendations are stale. The model is retrained daily using Vertex AI Pipelines. The pipeline completes successfully, but the endpoint continues to serve the old model. The team checks the pipeline logs and sees that the new model is uploaded to the Vertex AI Model Registry. The endpoint has traffic split set to 100% for the old model. The team needs to update the endpoint to serve the new model version. What should they do?
Check the pipeline for errors in the deployment step
Why wrong: Pipeline succeeded; no errors.
Re-upload the model with a different version ID
Why wrong: Model already uploaded; endpoint not updated.
Redeploy the same model to the endpoint
Why wrong: Would not serve new version.
Update the endpoint to deploy the new model version from the registry and adjust traffic split
Explicitly deploy new version to endpoint.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Update the endpoint to deploy the new model version from the registry and adjust traffic split
Option D is correct because the pipeline successfully uploaded the new model to the Vertex AI Model Registry, but the endpoint still has its traffic split configured to 100% for the old model. To serve the new model, the team must explicitly update the endpoint to deploy the new model version from the registry and adjust the traffic split to route 100% of traffic to it. This is a standard operational step in Vertex AI: uploading a model does not automatically update the endpoint's deployment or traffic allocation.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Check the pipeline for errors in the deployment step
Why it's wrong here
Pipeline succeeded; no errors.
Re-upload the model with a different version ID
Why it's wrong here
Model already uploaded; endpoint not updated.
Redeploy the same model to the endpoint
Why it's wrong here
Would not serve new version.
Update the endpoint to deploy the new model version from the registry and adjust traffic split
Why this is correct
Explicitly deploy new version to endpoint.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Google Cloud often tests the misconception that uploading a new model version to the registry automatically updates the endpoint's serving configuration, when in fact the traffic split must be explicitly adjusted to route requests to the new model.
Detailed technical explanation
In Vertex AI, an endpoint is a resource that can host one or more deployed models, each with a traffic percentage. When a new model version is uploaded to the Model Registry, it is not automatically associated with any endpoint; the endpoint's deployment configuration must be explicitly updated via the `deployModel` API or the console to add the new model version and set its traffic split. A common real-world scenario is when a CI/CD pipeline uploads a model but forgets to call the endpoint update step, leading to stale predictions even though the pipeline reports success.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
The correct answer is: Update the endpoint to deploy the new model version from the registry and adjust traffic split — Option D is correct because the pipeline successfully uploaded the new model to the Vertex AI Model Registry, but the endpoint still has its traffic split configured to 100% for the old model. To serve the new model, the team must explicitly update the endpoint to deploy the new model version from the registry and adjust the traffic split to route 100% of traffic to it. This is a standard operational step in Vertex AI: uploading a model does not automatically update the endpoint's deployment or traffic allocation.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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