MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A company wants to deploy a new model using a canary deployment strategy on SageMaker. Which two actions should they take? (Select TWO.)
⚠ Common exam trap
MLA-C01 often tests whether candidates confuse the mechanics of canary deployment (two variants + traffic weights) with supporting features like Model Monitor or Model Registry — the trap is selecting monitoring/governance options as if they were required to create the canary.
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
✓
Create a new endpoint with two production variants
Option C is correct because a SageMaker canary deployment is implemented by creating an endpoint configuration with two production variants (the existing/stable model and the new model) and deploying them behind a single endpoint, which is the foundational mechanism for splitting traffic between versions. Option E is correct because the canary pattern requires assigning initial traffic weights to those variants—typically a small percentage (e.g., 5%) to the new model and the remainder (e.g., 95%) to the stable model—so the new model receives limited live traffic before being promoted. Option A is not required for the deployment itself; Model Registry 'Approved' status is a governance/approval step, not a technical prerequisite for creating canary variants. Option B is not required because Model Monitor is used for detecting data/quality drift and bias, not for comparing model performance during a canary rollout. Option D is not required because data capture is an optional monitoring feature that logs request/response data, not a necessary action to perform 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.
- ✗
Register both models in the Model Registry with 'Approved' status
Why it's wrong here
Approving both models in the Model Registry merely records governance metadata; it does not create endpoint variants or split traffic. Canary deployment needs the new model deployed as a production variant with a small initial weight. Registry approval is correct for promoting a model through an approval workflow before deployment.
- ✗
Use SageMaker Model Monitor to compare model performance
Why it's wrong here
Model Monitor detects data drift and quality deviations on deployed endpoints; it does not shift or compare traffic between model variants during a canary rollout. It tempts because monitoring supports safe deployments, but traffic splitting requires a production variant with initial instance weight.
- ✓
Create a new endpoint with two production variants
Why this is correct
A canary deployment on SageMaker requires a single endpoint hosting two production variants, with traffic initially weighted heavily toward the existing model. Creating that endpoint satisfies the stem's requirement, letting a small percentage of inference requests shift to the new model.
- ✗
Enable data capture on the endpoint
Why it's wrong here
Data capture records request and response payloads for monitoring drift and quality; it does not shift traffic between model variants. Canary deployment requires production variants with initial weights, then UpdateEndpointWeightsAndCapacities to move traffic. Data capture would be correct when auditing endpoint inputs and outputs.
- ✓
Set the initial traffic weights for the variants (e.g., 95% and 5%)
Why this is correct
Canary deployment requires splitting live traffic between the old and new model variants, so setting initial weights such as 95%/5% establishes the small canary share. This directly satisfies the stem's requirement to shift traffic gradually, letting the team validate the new model before widening its allocation.
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Same concept, more angles
1 more way this is tested on MLA-C01
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 team wants to deploy a new model using a canary deployment strategy on SageMaker. Which TWO configurations are necessary? (Choose two.)
medium- A.Create a CloudWatch alarm to automatically rollback
- ✓ B.Set the initial traffic distribution (e.g., 90% old, 10% new)
- C.Enable data capture on the endpoint
- ✓ D.Create a new endpoint configuration with two production variants, each pointing to a different model
- E.Use SageMaker Model Registry to approve the new model
Why B: 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.
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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