AIF-C01 Fundamentals of AI and ML Practice Question
A SageMaker endpoint is configured with automatic scaling. The model's inference time is 50ms, and traffic increases gradually. What scaling metric should be used to add instances before latency increases?
⚠ Common exam trap
A common mix-up: candidates choose 'Concurrent requests' (Option B) thinking it directly measures load, but AWS SageMaker does not expose that metric for scaling; instead, 'Invocations per instance' is the correct metric that normalizes load per instance and enables proactive scaling.
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
✓
Invocations per instance
D is correct because 'Invocations per instance' is a custom metric that directly measures the number of inference requests each instance is handling. By setting a target value for this metric, the scaling policy can proactively add instances when the per-instance request count approaches a threshold, preventing latency increases before they occur. This is the recommended approach for SageMaker endpoints with gradual traffic increases, as it anticipates demand rather than reacting to latency spikes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Memory utilization
Why it's wrong here
Memory utilization is not a direct indicator of request load.
- ✗
Concurrent requests
Why it's wrong here
Concurrent requests can be a good metric but invocations per instance provides a more direct measure of throughput per instance.
- ✗
CPU utilization
Why it's wrong here
CPU utilization may lag behind traffic increases and not directly reflect request load.
- ✓
Invocations per instance
Why this is correct
Invocations per instance directly measures the load per instance, allowing proactive scaling before latency rises.
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