MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company has a real-time inference endpoint on Amazon SageMaker that uses a custom container. The endpoint is experiencing high latency and occasional 502 errors. The logs from the container show that the model inference time is low, but the overall response time is high. Which step is MOST likely to reduce the latency?
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
✓
Increase the number of instances and enable auto-scaling
Increasing the number of instances and enabling auto-scaling helps distribute the incoming request load, reducing queuing delays at the endpoint. The logs show inference time is low, so the high latency is likely due to request queuing behind other requests. Scaling out addresses this. Option A is wrong because batch transform is designed for offline, batch processing, not real-time inference. Option B is wrong because using a larger instance type may provide more compute but does not directly address queuing; it is also less cost-effective than scaling out. Option C is wrong because the model inference time is already low, so further optimization would have minimal impact on overall latency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to batch transform to process requests in batches
Why it's wrong here
Batch transform is not for real-time inference.
- ✗
Use a larger instance type for the endpoint
Why it's wrong here
Larger instances may help but scaling out is often more effective for handling concurrent requests.
- ✗
Optimize the model to reduce inference time
Why it's wrong here
Inference time is already low, so this won't reduce overall latency.
- ✓
Increase the number of instances and enable auto-scaling
Why this is correct
More instances can handle more concurrent requests, reducing queuing and latency.
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