MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company has deployed a model on SageMaker for real-time inference. The endpoint is experiencing high latency during traffic spikes. Which action should the company take to reduce latency?
⚠ Common exam trap
AWS often tests the misconception that improving per-request performance (e.g., via larger instances, Elastic Inference, or Neo compilation) is the solution for handling traffic spikes, when the actual need is horizontal scaling to increase request throughput capacity.
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
✓
Enable SageMaker endpoint auto-scaling
Enabling SageMaker endpoint auto-scaling allows the endpoint to dynamically adjust the number of instances based on incoming traffic, which directly reduces latency during spikes by ensuring sufficient compute capacity is available. Auto-scaling uses CloudWatch metrics (e.g., InvocationsPerInstance or latency) to trigger scale-out events, preventing queue buildup and response time degradation.
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 a larger instance type for the endpoint
Why it's wrong here
Larger instance may reduce latency but is not the best for spikes; auto-scaling is more cost-effective.
- ✗
Attach SageMaker Elastic Inference to the endpoint
Why it's wrong here
Elastic Inference reduces per-request latency but does not handle traffic spikes.
- ✓
Enable SageMaker endpoint auto-scaling
Why this is correct
Auto-scaling adds instances during spikes, reducing latency.
- ✗
Use SageMaker Neo to compile the model
Why it's wrong here
Neo optimizes for edge devices, not for server-side inference.
- ✗
Switch to SageMaker batch transform
Why it's wrong here
Batch transform is for offline inference, not real-time.
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