Question 963 of 1,672
MLS-C01 SageMaker Neo Practice Question
A machine learning engineer is deploying a custom XGBoost model for real-time inference on Amazon SageMaker. The model was trained using the SageMaker XGBoost built-in algorithm. The endpoint is deployed with an ml.m5.large instance and is receiving around 50 requests per second. The engineer notices that the endpoint's latency is around 200 ms, but the requirement is under 100 ms. The model's serialized format is a .tar.gz file. The engineer wants to reduce inference latency without modifying the model or retraining. What should the engineer do?
⚠ Common exam trap
Candidates may incorrectly choose Elastic Inference (B) thinking it speeds up all models, but it is only useful for deep learning models, not tree-based XGBoost.
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 SageMaker Neo to compile the model for the target instance.
SageMaker Neo compiles trained models to optimize them for target hardware, reducing inference latency without modifying the model. Option A is wrong because SageMaker Debugger is used for monitoring training jobs and debugging, not for optimizing inference code. Option B is wrong because SageMaker Elastic Inference attaches GPU acceleration, which is beneficial for deep learning models but not for XGBoost (a tree-based model). Option D is wrong because SageMaker Batch Transform is designed for batch predictions on large datasets, not for real-time inference with low latency requirements.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure SageMaker Debugger to optimize the inference code.
Why it's wrong here
SageMaker Debugger monitors training and can help debug training issues, but it does not optimize inference code or reduce latency.
- ✗
Use SageMaker Elastic Inference to attach an accelerator.
Why it's wrong here
SageMaker Elastic Inference attaches GPU acceleration for deep learning models, but XGBoost is a tree-based model that typically runs on CPU, so Elastic Inference is not suitable and will not reduce latency.
- ✓
Use SageMaker Neo to compile the model for the target instance.
Why this is correct
SageMaker Neo compiles the trained model to optimize it for the target hardware (ml.m5.large), which can improve inference speed and reduce latency without modifying the model.
- ✗
Use SageMaker Batch Transform instead of a real-time endpoint.
Why it's wrong here
SageMaker Batch Transform processes large batches of data asynchronously, not real-time. It does not affect the latency of a real-time endpoint.
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Last reviewed: Jun 20, 2026
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
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