Question 192 of 1,000
Serving and Scaling ModelseasyMultiple ChoiceObjective-mapped

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.

You have a Vertex AI endpoint that serves a model for real-time predictions. You want to update the model to a new version with zero downtime. Which approach should you take?

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

Deploy the new model version to the same endpoint and then set traffic to 100% for the new version.

Option B is correct because Vertex AI endpoints support canary deployments by allowing you to deploy a new model version to the same endpoint and then gradually shift traffic to it using the `traffic_split` parameter. Setting traffic to 100% for the new version after deployment ensures zero downtime, as the endpoint remains active and serves requests from the old version until the switch is complete.

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.

  • Delete the endpoint and recreate it with the new model.

    Why it's wrong here

    Causes downtime.

  • Deploy the new model version to the same endpoint and then set traffic to 100% for the new version.

    Why this is correct

    This allows zero-downtime deployment; the old version remains available during transition.

    Related concept

    Read the scenario before looking for a memorised answer.

  • Use Cloud Load Balancing to switch traffic between two endpoints.

    Why it's wrong here

    Unnecessary; Vertex AI endpoints handle versioning natively.

  • Create a new endpoint and update the client application to point to the new endpoint.

    Why it's wrong here

    Causes downtime and requires client changes.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates assume a new endpoint or load balancer is required for zero-downtime updates, but Vertex AI endpoints natively support traffic splitting between model versions on the same endpoint, making external components unnecessary.

Detailed technical explanation

How to think about this question

Under the hood, Vertex AI endpoints use a `traffic_split` map (e.g., `{'model1': 100, 'model2': 0}`) to route requests to different model versions deployed on the same endpoint. During a canary update, you can deploy the new model with 0% traffic, validate it, then atomically update the traffic split to 100% for the new version — this is a single API call that does not disrupt in-flight requests. In real-world scenarios, this is critical for A/B testing or gradual rollouts where you monitor metrics like latency or error rates before fully cutting over.

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?

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: Deploy the new model version to the same endpoint and then set traffic to 100% for the new version. — Option B is correct because Vertex AI endpoints support canary deployments by allowing you to deploy a new model version to the same endpoint and then gradually shift traffic to it using the `traffic_split` parameter. Setting traffic to 100% for the new version after deployment ensures zero downtime, as the endpoint remains active and serves requests from the old version until the switch is complete.

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.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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Last reviewed: Jul 4, 2026

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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.