easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A data science team needs to deploy a frequently…
A data science team needs to deploy a frequently updated PyTorch model for real-time inference. The model is retrained weekly and versioned using SageMaker Model Registry. Which deployment strategy minimizes downtime and allows easy rollback?
⚠ Common exam trap
Candidates often assume a single production variant with endpoint updates is sufficient, overlooking the downtime and rollback limitations, while the canary deployment pattern with multiple variants directly addresses the requirements for minimal downtime and easy rollback.
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
✓
Configure SageMaker endpoints with multiple production variants and use canary deployment to shift traffic gradually.
SageMaker endpoints with multiple production variants enable canary deployment, which shifts traffic gradually from the old model to the new one. This minimizes downtime by keeping both variants active during the transition and allows easy rollback by simply redirecting all traffic back to the previous variant if issues arise.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy the model on an EC2 instance behind an Application Load Balancer and manually update the instance with the new model version.
Why it's wrong here
Manual updates increase downtime and risk of errors.
- ✗
Deploy the model using AWS Lambda with a container image and trigger via API Gateway.
Why it's wrong here
Lambda is not optimized for large model inference and has cold start issues.
- ✓
Configure SageMaker endpoints with multiple production variants and use canary deployment to shift traffic gradually.
Why this is correct
Canary deployment allows gradual traffic shift, minimizing downtime and enabling rollback.
- ✗
Use SageMaker hosting with a single production variant and update the endpoint with a new model configuration each week.
Why it's wrong here
Updating the single variant causes downtime during update.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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