easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A company has a SageMaker endpoint that uses a…
A company has a SageMaker endpoint that uses a trained model to classify images. The endpoint is experiencing high latency and the team suspects it is due to the model size. Which action can the team take to reduce latency without significantly impacting accuracy?
⚠ Common exam trap
AWS often tests the misconception that converting to an open format like ONNX inherently optimizes performance, when in reality it is just a serialization format and requires a separate compilation step (e.g., Neo) to reduce 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
✓
Use SageMaker Neo to compile the model for the target instance
SageMaker Neo compiles trained models into an optimized binary for the target hardware, applying techniques like operator fusion, memory layout optimization, and quantization. This reduces model size and inference latency while preserving accuracy, making it the correct choice for addressing high latency caused by model size.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to a compute-optimized instance type
Why it's wrong here
This may not directly address model size overhead.
- ✓
Use SageMaker Neo to compile the model for the target instance
Why this is correct
Neo optimizes model inference for specific hardware, reducing latency.
- ✗
Reduce the batch size of inference requests
Why it's wrong here
Reducing batch size can increase latency due to more invocations.
- ✗
Convert the model to ONNX format
Why it's wrong here
ONNX is a format, not an optimization for latency.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-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 MLA-C01 exam.