MLS-C01 Modeling Practice Question
A data scientist trains a gradient boosting model on a large dataset using SageMaker. The training completes successfully, but when deploying the model to a real-time endpoint, inference latency is too high. Which change is MOST likely to reduce latency without significant accuracy loss?
⚠ Common exam trap
It's easy for candidates to confuse scaling the endpoint (Option A) as the primary fix for latency, when the real issue is model complexity that can be reduced through pruning without significant accuracy loss.
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
✓
Prune the trees by removing nodes with low importance
Pruning trees by removing nodes with low importance reduces the model's complexity, which directly decreases inference latency because fewer decision paths need to be evaluated. In gradient boosting, this can be done with minimal accuracy loss if the removed nodes correspond to splits that contribute little to the overall prediction, as measured by feature importance or gain.
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 for the endpoint
Why it's wrong here
Larger instance may not address model complexity.
- ✓
Prune the trees by removing nodes with low importance
Why this is correct
Pruning reduces model size and inference time.
- ✗
Increase the number of trees in the ensemble
Why it's wrong here
More trees increase latency.
- ✗
Use SageMaker Batch Transform instead of real-time
Why it's wrong here
Batch Transform is for offline predictions, not reducing latency for real-time.
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