MLS-C01 Practice Question: Machine Learning Implementation and Operations
Exhibit
Refer to the exhibit.
{
"AlarmName": "HighLatency",
"MetricName": "ModelLatency",
"Namespace": "AWS/SageMaker",
"Statistic": "p99",
"Period": 60,
"EvaluationPeriods": 2,
"Threshold": 500,
"ComparisonOperator": "GreaterThanThreshold"
}A SageMaker endpoint has a CloudWatch alarm configured as shown in the exhibit. The alarm fires when the p99 latency exceeds 500 ms for two consecutive minutes. Which action should the data scientist take to reduce latency?
⚠ Common exam trap
A common mix-up: candidates confuse latency reduction with throughput improvements, and incorrectly choose batch size increase or GPU switching, not realizing that scaling out is the direct remedy for high tail latency under sustained load.
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 behind the endpoint
Increasing the number of instances behind the endpoint adds more compute capacity to handle the inference requests, which directly reduces the queuing and processing time for each request. Since the alarm triggers when p99 latency exceeds 500 ms for two consecutive minutes, scaling out horizontally distributes the load and lowers tail 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.
- ✓
Increase the number of instances behind the endpoint
Why this is correct
More instances distribute load, reducing latency.
- ✗
Increase the batch size in the inference request
Why it's wrong here
Larger batch size may increase latency.
- ✗
Use SageMaker asynchronous inference instead of real-time
Why it's wrong here
Async inference is not for low latency.
- ✗
Switch to GPU instances even if the model does not require GPU
Why it's wrong here
GPU may not reduce latency if not utilized.
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