MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A data scientist wants to compare the performance of two model versions (V1 and V2) in production by splitting traffic between them. They want to gradually increase the percentage of traffic to the new version while monitoring metrics. Which SageMaker feature enables this?
⚠ Common exam trap
The trap is that 'canary deployment' sounds like a distinct SageMaker feature, but in SageMaker it is implemented via production variants and traffic splitting, so candidates who pick the canary-named option miss the actual configuration mechanism.
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
✓
SageMaker production variants with traffic splitting
SageMaker production variants with traffic splitting let you host multiple model versions behind a single endpoint and assign a percentage of invocations to each variant. To compare V1 and V2 and gradually shift traffic, you create an endpoint configuration with two production variants and set the initial traffic distribution (e.g., 90/10), then update the weights as you gain confidence. This is the native SageMaker mechanism for A/B comparison and gradual rollout.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
SageMaker production variants with traffic splitting
Why this is correct
SageMaker production variants let a single endpoint host multiple model versions with weighted traffic distribution, so the data scientist can shift percentages gradually while monitoring metrics. This satisfies the stem's requirement for controlled A/B comparison and progressive rollout between V1 and V2.
- ✗
SageMaker shadow testing
Why it's wrong here
Shadow testing mirrors production requests to the new model without returning its responses to users, so no live traffic percentage reaches V2 and no user-facing metrics can be compared. It is tempting because shadow mode safely evaluates a new version, but it suits pre-release validation, not gradual production traffic shifting.
- ✗
SageMaker blue/green deployment
Why it's wrong here
Blue/green deployment swaps traffic between two identical environments after validation, typically shifting 100% at cutover rather than supporting incremental percentage splits with live metric comparison. It is tempting because blue/green does run two versions concurrently, but its purpose is atomic release cutover, not gradual traffic weighting.
- ✗
SageMaker canary deployment
Why it's wrong here
Canary deployment shifts all traffic to the new version once validation passes; it does not support continuous percentage-based splitting with metric comparison across both versions. It is tempting because canary releases do gradually ramp traffic, but the stem requires sustained A/B comparison, which SageMaker's production variant traffic distribution provides.
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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 Amazon Web Services exam blueprint
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services 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 MLA-C01 exam.