MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A team wants to deploy a new model using a canary deployment strategy on SageMaker. Which TWO configurations are necessary? (Choose two.)
⚠ Common exam trap
The trap is that several options are good practices (alarms, data capture, registry approval) but not necessary configurations, so candidates who conflate best practices with requirements select the wrong two.
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
✓
Set the initial traffic distribution (e.g., 90% old, 10% new)
Option B is correct because a SageMaker canary deployment is defined by specifying the initial traffic split between the existing (old) variant and the new variant, for example 90% to the old model and 10% to the new model, via the endpoint configuration's variant weights. Option D is correct because canary deployment requires a new endpoint configuration that contains two production variants, each referencing a different model, so traffic can be shifted between the old and new versions. Option A is not required, since CloudWatch alarms and automatic rollback are optional safeguards rather than mandatory canary configuration elements. Option C is not required, as data capture is an optional monitoring feature and not part of the canary deployment definition. Option E is not required, because Model Registry approval is a governance step and not a necessary configuration for performing a canary deployment.
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 a CloudWatch alarm to automatically rollback
Why it's wrong here
A CloudWatch alarm performs automated rollback after a metric breach; it does not create the canary traffic split itself, so it cannot satisfy the deployment configuration requirement. It is tempting because alarms are genuinely part of production canary deployments, and would be correct if the question asked how to automatically revert a failing endpoint.
- ✓
Set the initial traffic distribution (e.g., 90% old, 10% new)
Why this is correct
Setting the initial traffic distribution defines how much live inference traffic the new model variant receives from the outset. SageMaker's canary strategy requires this split to route a small percentage to the new variant while the remainder stays on the current one, satisfying the gradual, controlled rollout the stem demands.
- ✗
Enable data capture on the endpoint
Why it's wrong here
Data capture records request and response payloads for monitoring and drift analysis; it does not distribute inference traffic across variants, so it establishes no canary. It is tempting because data capture is genuinely recommended for canary endpoints, and would be the right choice if the question asked how to collect inference data for later evaluation.
- ✓
Create a new endpoint configuration with two production variants, each pointing to a different model
Why this is correct
Two production variants in one endpoint configuration let SageMaker split invocation traffic between the existing model and the new one, which is the mechanism canary deployments require. Weighted traffic distribution across variants satisfies the gradual-shift constraint, so you can ramp the new model's share while monitoring.
- ✗
Use SageMaker Model Registry to approve the new model
Why it's wrong here
Model Registry approval governs model governance and promotion workflow, not traffic splitting between variants, so it configures no canary percentage. It is tempting because registry approval is genuinely required for a full production release pipeline, and would be the right answer if the question asked how to gate a model's promotion to production.
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JA
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
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