MLS-C01 Practice Question: Machine Learning Implementation and Operations
Which TWO actions can reduce inference latency for a SageMaker real-time endpoint? (Choose 2.)
⚠ Common exam trap
A common trap in AWS exams is the distinction between scaling for throughput (adding instances) vs. scaling for latency (increasing instance size or optimizing the model). Candidates mistakenly choose 'add more instances' thinking it reduces per-request 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
✓
Choose a larger instance type with more compute capacity.
Choosing a larger instance type with more compute capacity (Option A) reduces inference latency because it provides more CPU/GPU and memory resources, enabling the model to process each request faster. This directly decreases the time per inference, which is the primary driver of latency for real-time endpoints.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Choose a larger instance type with more compute capacity.
Why this is correct
More compute reduces per-request latency.
- ✗
Add more instances behind the endpoint.
Why it's wrong here
Scaling out increases throughput, not per-request latency.
- ✗
Use batch transform instead.
Why it's wrong here
Batch transform is for offline processing, not real-time.
- ✓
Compile the model using SageMaker Neo.
Why this is correct
Neo optimizes the model for faster inference.
- ✗
Switch to asynchronous inference.
Why it's wrong here
Asynchronous inference has higher latency but better for large payloads.
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