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MLA-C01 Practice Question: An ML team at a financial services company has…
An ML team at a financial services company has developed a fraud detection model using Amazon SageMaker. The model is currently deployed to a production endpoint with a single variant using the previous model version. The team wants to deploy a new model version with a canary deployment where 10% of traffic goes to the new version and 90% remains on the old version for 30 minutes before shifting all traffic to the new version if no issues are detected. Which step is essential to achieve this safe rollout?
⚠ Common exam trap
Many candidates think canary deployments require manual traffic splitting or separate endpoints, but SageMaker's native 'BlueGreenUpdatePolicy' with 'TrafficRoutingConfiguration' automates the entire process, including traffic shifting and rollback, without needing custom code or DNS manipulation.
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
✓
Update the endpoint with a new production variant for the new model version and set the 'InitialVariantWeight' to 10 for the new variant and 90 for the old variant, specifying a 'BlueGreenUpdatePolicy' with a 'TrafficRoutingConfiguration' for canary.
It uses the SageMaker endpoint update with a new production variant and sets 'InitialVariantWeight' to 10 for the new model and 90 for the old model, which routes 10% of traffic to the new version. Additionally, specifying a 'BlueGreenUpdatePolicy' with a 'TrafficRoutingConfiguration' for canary enables the automatic shift of all traffic to the new variant after 30 minutes if no issues are detected, achieving the desired safe 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.
- ✗
Use the 'Deploy' method on the model object with the 'mode' parameter set to 'canary' within the built-in XGBoost algorithm container.
Why it's wrong here
The built-in algorithms do not have a 'canary' deployment mode; canary deployments are configured via the endpoint update configuration.
- ✓
Update the endpoint with a new production variant for the new model version and set the 'InitialVariantWeight' to 10 for the new variant and 90 for the old variant, specifying a 'BlueGreenUpdatePolicy' with a 'TrafficRoutingConfiguration' for canary.
Why this is correct
This configuration uses SageMaker's blue/green deployment with canary traffic shifting, which is the correct approach.
- ✗
Ensure the endpoint is hosted on at least two instances to enable load balancing, then deploy the new model version as a separate variant and manually adjust the endpoint's DNS to split traffic.
Why it's wrong here
Manual DNS changes are not recommended; SageMaker canary deployments automatically handle traffic splitting using endpoint variant weights.
- ✗
Deploy the new model as a separate endpoint and use a SageMaker predictor to randomly route 10% of inference requests to the new endpoint.
Why it's wrong here
SageMaker does not support client-side routing via the predictor for canary deployments; it relies on endpoint variant weights.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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