MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A company uses SageMaker Neo to compile a trained model for deployment on edge devices. What is the primary benefit of using Neo?
⚠ Common exam trap
MLA-C01 often tests confusion between SageMaker features — candidates pick Model Monitor or Serverless Inference when the question is specifically about compiling models for edge hardware with Neo.
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
✓
It reduces model size and improves inference speed on target hardware
SageMaker Neo compiles trained models into optimized executables for specific target hardware (CPU, GPU, or edge accelerators), producing smaller artifacts and faster inference by leveraging hardware-specific instruction sets. It is designed for edge and constrained deployments where latency, memory, and compute are limited. Neo does not handle monitoring, retraining, or endpoint provisioning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It monitors model drift in production
Why it's wrong here
Neo does not observe production traffic or detect drift; it only compiles a static model for a target platform. Drift monitoring is the right tool when deployed model accuracy degrades over time and you need alerts on data distribution shifts.
- ✓
It reduces model size and improves inference speed on target hardware
Why this is correct
SageMaker Neo compiles models into optimised executables for specific target hardware, reducing model size and improving inference latency and throughput on edge devices. This satisfies the edge deployment constraint, where resource limits make unoptimised frameworks impractical.
- ✗
It automatically retrains the model on new data
Why it's wrong here
Neo compiles trained models into optimised executables for specific edge hardware targets; it performs no training, so retraining on new data falls outside its scope entirely. Retraining is tempting because edge models often drift, but that requires SageMaker training jobs or Pipelines, not Neo's compilation step.
- ✗
It provides a serverless inference endpoint
Why it's wrong here
Neo compiles models for target hardware, optimising latency and footprint on edge devices; it does not host endpoints. Serverless inference is the correct choice when you want SageMaker to manage scaling and infrastructure without provisioning instances.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.