MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A data scientist deploys a model and wants to monitor the endpoint's invocation latency. They notice that the CloudWatch metric 'ModelLatency' is high, but 'OverheadLatency' is low. Which statement correctly interprets these metrics?
⚠ Common exam trap
Many exam-takers confuse 'ModelLatency' with overall endpoint latency and assume any high latency is due to infrastructure or scaling issues, when in fact the metric explicitly isolates the model's own inference time from overhead.
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
✓
The model inference time is the bottleneck; consider optimizing the model or using a faster instance type
The 'ModelLatency' metric measures the time taken by the SageMaker model container to process a single request, including inference and any preprocessing/postprocessing within the container. 'OverheadLatency' measures the time spent on SageMaker infrastructure (e.g., network I/O, request queuing, and response handling). When ModelLatency is high and OverheadLatency is low, the bottleneck is clearly the model inference time itself, not the infrastructure overhead. Therefore, optimizing the model (e.g., quantization, pruning) or upgrading to a faster instance type (e.g., GPU vs. CPU) is the correct remediation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The SageMaker overhead is causing the delay; check endpoint configuration
Why it's wrong here
OverheadLatency is low, so overhead is not the issue.
- ✓
The model inference time is the bottleneck; consider optimizing the model or using a faster instance type
Why this is correct
High ModelLatency indicates inference time is the issue.
- ✗
The endpoint is overloaded; increase the number of instances
Why it's wrong here
High ModelLatency is not necessarily due to overload; it could be model slowness. Overload would manifest as increased 5XX errors or overall latency.
- ✗
The network latency is high; move the endpoint closer to clients
Why it's wrong here
Network latency is not reflected in ModelLatency or OverheadLatency.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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