PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
An ML engineer needs to deploy a model to an endpoint and gradually shift traffic from the previous version (champion) to a new version (challenger) for A/B testing. How should they configure the endpoint?
⚠ Common exam trap
PMLE often tests deployment strategies, and candidates may confuse infrastructure-level canary deployments (e.g., Cloud Run) with model-level traffic splitting, or they may choose manual switching, missing the need for gradual, controlled experimentation.
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 versions to the same endpoint and set traffic splitting
To gradually shift traffic between a champion and challenger model for A/B testing, the engineer should deploy both versions to the same endpoint and configure traffic splitting. This allows the endpoint to route a percentage of requests to each version, enabling controlled experimentation and rollback. This is the standard pattern for A/B testing on managed ML platforms.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a canary deployment with Cloud Run
Why it's wrong here
Cloud Run is Google Cloud's container hosting service, not an Amazon SageMaker endpoint configuration; it cannot deploy SageMaker model variants. It is tempting because canary deployment is the right pattern, but the mechanism must be SageMaker production variants with traffic weights. Cloud Run would be correct for canary-releasing a containerised service on Google Cloud.
- ✗
Manually update the endpoint to point to the challenger after testing
Why it's wrong here
Manually repointing the endpoint replaces the champion outright, so no gradual traffic shift or simultaneous A/B comparison occurs. It is tempting because it is simple, but production variants with initial traffic weights let you split traffic between champion and challenger. Manual switching suits a full cutover after testing concludes, not during it.
- ✗
Create a new endpoint for the challenger and route traffic via load balancer
Why it's wrong here
A separate endpoint behind a load balancer splits traffic outside SageMaker's variant weights, so you cannot shift percentages per variant or gather unified invocation metrics. It is tempting because load balancers are familiar, but a single endpoint with multiple production variants supports weighted traffic distribution. Separate endpoints suit isolating wholly independent models.
- ✓
Deploy both versions to the same endpoint and set traffic splitting
Why this is correct
Deploying both versions to one endpoint with traffic splitting lets the engineer route a defined percentage to the challenger while the champion serves the remainder, enabling gradual A/B comparison. Separate endpoints would not permit proportional traffic distribution between versions.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.