- A
Create two separate endpoints, one for each version, and use a separate load balancer to route a percentage of requests to the canary endpoint.
Why wrong: This adds complexity and does not use the native splitting feature; also, traffic management is more manual.
- B
Deploy both models to the same endpoint and configure traffic splitting percentages using the Vertex AI console or API.
Vertex AI endpoints natively support traffic splitting between deployed models, allowing gradual rollout and canary testing.
- C
Use Cloud Armor with weighted backend services to route a portion of requests to the canary version.
Why wrong: Cloud Armor is a security policy tool, not for traffic routing between model versions.
- D
Implement feature flags in the application code to randomly select the model version for each prediction request.
Why wrong: This would require modifying the application to call different endpoints, not leveraging Vertex AI's capabilities.
Traffic Splitting and Canary Deployments on Vertex AI Endpoints
This PDE practice question tests your understanding of operationalizing machine learning 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 manage a team that deploys multiple versions of a computer vision model for A/B testing on Vertex AI Endpoints. You need to route a small percentage of traffic to a canary version while the rest goes to the stable version. You also need to gradually increase the canary traffic over time based on performance metrics. Which approach should you take?
Quick Answer
The correct approach is to deploy both models to the same Vertex AI endpoint and configure traffic splitting percentages using the console or API. This works because Vertex AI Endpoints natively support weighted routing between model versions, allowing you to assign a small percentage of traffic to a canary version while the rest flows to the stable version, and then gradually adjust those splits programmatically as performance metrics improve. On the Google Professional Data Engineer exam, this scenario tests your understanding of managed model serving versus lower-level networking—a common trap is choosing Cloud Load Balancing’s weighted routing, which is designed for infrastructure traffic, not model version management. Another trap is splitting across two separate endpoints, which prevents single-endpoint traffic control. Remember the key distinction: Vertex AI endpoints handle model version traffic splitting directly, so you never need to involve load balancers or feature flags for canary deployments. Memory tip: think “one endpoint, many versions, split by percentage.”
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 both models to the same endpoint and configure traffic splitting percentages using the Vertex AI console or API.
Vertex AI Endpoints natively support traffic splitting between model versions deployed to the same endpoint. This allows you to assign a percentage of traffic (e.g., 5%) to a canary version and the remainder to the stable version, and then adjust the split over time via the console or API as performance metrics dictate. This approach avoids the complexity and latency of external load balancers or application-level routing.
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.
- ✗
Create two separate endpoints, one for each version, and use a separate load balancer to route a percentage of requests to the canary endpoint.
Why it's wrong here
This adds complexity and does not use the native splitting feature; also, traffic management is more manual.
- ✓
Deploy both models to the same endpoint and configure traffic splitting percentages using the Vertex AI console or API.
Why this is correct
Vertex AI endpoints natively support traffic splitting between deployed models, allowing gradual rollout and canary testing.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Cloud Armor with weighted backend services to route a portion of requests to the canary version.
Why it's wrong here
Cloud Armor is a security policy tool, not for traffic routing between model versions.
- ✗
Implement feature flags in the application code to randomly select the model version for each prediction request.
Why it's wrong here
This would require modifying the application to call different endpoints, not leveraging Vertex AI's capabilities.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the misconception that you need an external load balancer or separate endpoints for canary deployments, when in fact Vertex AI's native traffic splitting is the correct and simplest approach.
Detailed technical explanation
How to think about this question
Vertex AI traffic splitting works by assigning a percentage weight to each deployed model version on the endpoint; the prediction request router uses these weights to probabilistically direct requests. This is implemented at the infrastructure layer, so there is no overhead on the client side. A real-world scenario is gradually ramping up a canary from 1% to 100% while monitoring latency and error rates, then promoting the canary to stable with zero downtime.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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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: Deploy both models to the same endpoint and configure traffic splitting percentages using the Vertex AI console or API. — Vertex AI Endpoints natively support traffic splitting between model versions deployed to the same endpoint. This allows you to assign a percentage of traffic (e.g., 5%) to a canary version and the remainder to the stable version, and then adjust the split over time via the console or API as performance metrics dictate. This approach avoids the complexity and latency of external load balancers or application-level routing.
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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Last reviewed: Jul 4, 2026
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