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 company deploys a model to Vertex AI Endpoint. They want to run a canary deployment to test a new model version with 10% of traffic. How should they configure this?
Deploy to a new endpoint and update the application to call both
Why wrong: Manual and error-prone.
Use Cloud Load Balancing to route traffic
Why wrong: Not designed for Vertex AI endpoints.
Deploy the new model to the same endpoint and set traffic split
Traffic splitting allows canary.
Deploy to Cloud Run and use gradual rollout
Why wrong: Cloud Run is not for Vertex AI models.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Deploy the new model to the same endpoint and set traffic split
Option C is correct because Vertex AI Endpoints natively support traffic splitting between model versions deployed to the same endpoint. By deploying the new model version to the same endpoint and setting a traffic split of 10% to the new version and 90% to the current version, the company can perform a canary deployment without changing the application code or infrastructure.
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.
Deploy to a new endpoint and update the application to call both
Why it's wrong here
Manual and error-prone.
Use Cloud Load Balancing to route traffic
Why it's wrong here
Not designed for Vertex AI endpoints.
Deploy the new model to the same endpoint and set traffic split
Why this is correct
Traffic splitting allows canary.
Related concept
Read the scenario before looking for a memorised answer.
Deploy to Cloud Run and use gradual rollout
Why it's wrong here
Cloud Run is not for Vertex AI models.
Common exam traps
Google Cloud often tests the misconception that canary deployments require separate endpoints or external load balancers, when in fact Vertex AI Endpoints provide a built-in traffic splitting feature that handles this at the model version level.
Detailed technical explanation
Vertex AI Endpoints use a traffic split configuration that is applied at the model deployment level, allowing you to assign a percentage of inference requests to each model version. This is implemented via the `traffic_split` parameter in the `DeployModel` API call, where you specify a dictionary mapping model version IDs to their respective traffic percentages. In a real-world scenario, you can monitor the canary version's performance metrics (e.g., latency, error rate, prediction accuracy) before gradually increasing its traffic share to 100%.
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: Deploy the new model to the same endpoint and set traffic split — Option C is correct because Vertex AI Endpoints natively support traffic splitting between model versions deployed to the same endpoint. By deploying the new model version to the same endpoint and setting a traffic split of 10% to the new version and 90% to the current version, the company can perform a canary deployment without changing the application code or infrastructure.
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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