MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company uses SageMaker to host a real-time inference endpoint. The endpoint is receiving a large number of requests, but the latency is higher than expected. The data scientist observes that the CPU utilization is low but memory utilization is high. Which action should be taken to reduce 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
✓
Switch to an instance type with more memory or optimize the model to reduce memory footprint.
High memory utilization indicates the model is memory-bound. Increasing instance memory or optimizing the model to reduce memory footprint can reduce latency. Option B is wrong because VPC traffic mirroring is used for network diagnostics, not for addressing memory bottlenecks. Option C is wrong because CPU utilization is low, so adding more vCPUs would not help; the bottleneck is memory, not CPU. Option D is wrong because increasing the number of instances can improve throughput but does not directly reduce per-request latency for a memory-bound model; it may also increase cost.
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 an instance type with more memory or optimize the model to reduce memory footprint.
Why this is correct
Addresses memory bottleneck.
- ✗
Enable VPC traffic mirroring to diagnose network issues.
Why it's wrong here
Network is not indicated as a problem.
- ✗
Use an instance type with more vCPUs.
Why it's wrong here
Low CPU utilization indicates CPU is not the bottleneck.
- ✗
Increase the number of instances in the endpoint.
Why it's wrong here
May help throughput but not per-request latency.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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