MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is using SageMaker to deploy a model for real-time inference. The model requires low latency, and the company wants to test the endpoint before production. Which approach should be used to validate endpoint performance?
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
✓
Use SageMaker's built-in shadow testing or load testing features.
SageMaker provides features like shadow testing, which allows you to test a new variant alongside the existing production variant without impacting live traffic, and integration with load testing tools to simulate traffic and measure latency before full production deployment. Option A is incorrect because CloudWatch Synthetics is used for monitoring endpoint health and availability, not for pre-production load or performance testing. Option B is incorrect because offline batch evaluation assesses model accuracy on a static dataset, but does not test real-time inference performance metrics such as latency and throughput. Option C is incorrect because deploying directly to production and monitoring exposes users to potential performance issues; pre-production validation should be conducted first.
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 CloudWatch Synthetics to create a canary.
Why it's wrong here
Not designed for SageMaker endpoints.
- ✗
Perform offline batch evaluation on a test dataset.
Why it's wrong here
Does not test inference latency.
- ✗
Deploy to production and monitor using CloudWatch.
Why it's wrong here
No pre-production validation.
- ✓
Use SageMaker's built-in shadow testing or load testing features.
Why this is correct
Allows traffic simulation and latency measurement.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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