MLS-C01 Practice Question: Machine Learning Implementation and Operations
A deployed SageMaker endpoint is returning high latency. The model is a scikit-learn Random Forest. Which action is most likely to reduce latency?
⚠ Common exam trap
Many candidates confuse latency (per-request time) with throughput (requests per second) and incorrectly choose scaling out instances (Option C), or assume GPU acceleration universally speeds up inference (Option D), ignoring that scikit-learn models are CPU-only.
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
✓
Reduce the number of trees in the ensemble
Reducing the number of trees in a Random Forest ensemble directly decreases the total number of decision paths that must be evaluated per inference request. Since each tree contributes additively to the prediction time, fewer trees means fewer sequential or parallel evaluations, which lowers the per-request latency at the cost of some model accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Reduce the number of trees in the ensemble
Why this is correct
Fewer trees reduce computation time per inference.
- ✗
Prune decision trees in the model
Why it's wrong here
Pruning can reduce size but may hurt accuracy; not standard practice.
- ✗
Increase the number of instances behind the endpoint
Why it's wrong here
More instances handle more requests but may not reduce latency per request.
- ✗
Switch to a GPU instance type
Why it's wrong here
GPU acceleration is not beneficial for Random Forest inference.
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