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Question 290 of 1,000

Operationalizing machine learning models →mediumMultiple ChoiceObjective-mapped

Canary Deployment Using Traffic Split on Vertex AI Endpoints

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 machine learning team wants to deploy a new model version for canary testing, where only 5% of traffic is routed to the new version. Which Vertex AI endpoint configuration supports this?

A
Have the client application randomly select which model to call with 5% probability.
Why wrong: This puts the logic on the client side and is not manageable at the server level.
B
Deploy the new version to a separate endpoint and direct 5% of users via a load balancer.
Why wrong: This adds complexity and does not leverage Vertex AI's built-in traffic splitting.
C
Configure the endpoint with traffic split: 95% to old version, 5% to new version.
Vertex AI endpoints allow splitting traffic between deployed models; the platform handles routing.
D
Use an A/B testing framework outside of Vertex AI to compare results.
Why wrong: This is a process, not a deployment configuration for traffic routing.

Full breakdown with real-world context →

Quick Answer

The correct answer is to configure the endpoint with a traffic split of 95% to the old version and 5% to the new version. This works because Vertex AI endpoints natively support traffic splitting between model versions, allowing you to route a precise percentage of inference requests to each deployed model without managing separate infrastructure. On the Google Professional Data Engineer exam, this tests your understanding of MLOps deployment strategies and the specific configuration options within Vertex AI—a common trap is confusing a canary deployment with creating a completely separate endpoint for testing, which defeats the purpose of gradual, controlled rollout. Remember that traffic split is a configuration property of a single endpoint, not a separate deployment. A useful memory tip: think of it as a "95/5 faucet" where you simply adjust the knob to control the flow between old and new versions, keeping the pipeline unified.

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

✓

Configure the endpoint with traffic split: 95% to old version, 5% to new version.

Vertex AI endpoints natively support traffic splitting, allowing you to route a specified percentage of requests to different model versions deployed on the same endpoint. By configuring a traffic split of 95% to the old version and 5% to the new version, you can perform canary testing without additional infrastructure or client-side logic. This is the correct and simplest approach within Vertex AI.

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.

✗
Have the client application randomly select which model to call with 5% probability.
Why it's wrong here
This puts the logic on the client side and is not manageable at the server level.
✗
Deploy the new version to a separate endpoint and direct 5% of users via a load balancer.
Why it's wrong here
This adds complexity and does not leverage Vertex AI's built-in traffic splitting.
✓
Configure the endpoint with traffic split: 95% to old version, 5% to new version.
Why this is correct
Vertex AI endpoints allow splitting traffic between deployed models; the platform handles routing.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use an A/B testing framework outside of Vertex AI to compare results.
Why it's wrong here
This is a process, not a deployment configuration for traffic routing.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates may think canary testing requires external tools or client-side logic, but Vertex AI's built-in traffic splitting is the intended and simplest method for this purpose.

Detailed technical explanation

How to think about this question

Vertex AI traffic splitting works by assigning a percentage of incoming prediction requests to each deployed model version based on the configured traffic split values. The traffic split is applied at the endpoint level, and the routing is handled transparently by the Vertex AI serving infrastructure, ensuring consistent and deterministic behavior. In a real-world scenario, you can monitor the canary version's performance metrics (e.g., latency, error rate) in real time and adjust the traffic split gradually or roll back if issues are detected, all without redeploying or changing client code.

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 PDE question test?

Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Configure the endpoint with traffic split: 95% to old version, 5% to new version. — Vertex AI endpoints natively support traffic splitting, allowing you to route a specified percentage of requests to different model versions deployed on the same endpoint. By configuring a traffic split of 95% to the old version and 5% to the new version, you can perform canary testing without additional infrastructure or client-side logic. This is the correct and simplest approach within Vertex AI.

What should I do if I get this PDE 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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Same concept, more angles

1 more ways this is tested on PDE

These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.

Variation 1. 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?

medium

A.Deploy to a new endpoint and update the application to call both
B.Use Cloud Load Balancing to route traffic
✓ C.Deploy the new model to the same endpoint and set traffic split
D.Deploy to Cloud Run and use gradual rollout

Why C: 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.

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