MLS-C01 Practice Question: Machine Learning Implementation and Operations
Exhibit
2023-01-01 12:00:00,000 - ERROR - Model prediction took 15 ms for request ID abc123
Refer to the exhibit. A data scientist is reviewing CloudWatch logs for a SageMaker real-time endpoint. The log shows that a prediction took 15 ms. The endpoint is configured with an ml.c5.large instance and the model is a small scikit-learn model. The latency requirement is under 10 ms. Which action would most likely reduce the latency?
⚠ Common exam trap
Test-takers frequently confuse horizontal scaling (adding instances) with reducing latency, but horizontal scaling only improves throughput, not the per-request response time, which is the key metric in this question.
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
✓
Use a larger instance type
The latency of 15 ms exceeds the 10 ms requirement, indicating that the current ml.c5.large instance lacks sufficient compute resources (CPU) to process predictions quickly enough. Upgrading to a larger instance type (e.g., ml.c5.xlarge or ml.c5.2xlarge) provides more CPU capacity, reducing inference time by allowing the model to compute predictions faster. This directly addresses the bottleneck for a small scikit-learn model, which is CPU-bound and benefits from increased compute power.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a larger instance type
Why this is correct
More CPU power reduces latency.
- ✗
Add more instances to the endpoint
Why it's wrong here
Adding instances increases throughput, not reduces latency.
- ✗
Change the model to a TensorFlow model
Why it's wrong here
Framework change may not reduce latency.
- ✗
Enable SageMaker Batch Transform
Why it's wrong here
Batch transform is not real-time.
- ✗
Increase the batch size for inference
Why it's wrong here
Batch size is for batch transform, not real-time.
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