easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A company has trained a custom model using…
A company has trained a custom model using PyTorch on Amazon SageMaker. The model achieves high accuracy, but the inference latency on a real-time endpoint is above the required 100ms SLA. The model is a large neural network with many layers. The company wants to reduce latency without significantly impacting accuracy. Which approach should the machine learning engineer take?
⚠ Common exam trap
AWS often tests the misconception that simply scaling up hardware (Option C) or batching (Option A) is the primary solution for latency issues, when in fact model compilation (Option B) is the targeted optimization for inference speed without 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
✓
Use SageMaker Neo to compile the model for the target hardware.
SageMaker Neo compiles trained models into an optimized binary for the target hardware (e.g., CPU, GPU, or Inferentia). It applies graph-level optimizations, operator fusion, and quantization-aware tuning to reduce inference latency while preserving model accuracy. This directly addresses the need to lower latency below 100ms without retraining or sacrificing significant 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 batch size used during inference.
Why it's wrong here
Batch size reduction affects throughput but may not significantly reduce per-request latency.
- ✓
Use SageMaker Neo to compile the model for the target hardware.
Why this is correct
Neo applies hardware-specific optimizations that reduce latency without retraining.
- ✗
Increase the instance size of the endpoint.
Why it's wrong here
Larger instances may reduce latency but increase cost and may not be the most effective.
- ✗
Implement a cache for frequent inference requests.
Why it's wrong here
Caching reduces latency for repeated requests, but not for unique ones; does not address overall latency.
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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.