MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
An ML engineer needs to compile a trained TensorFlow model to run efficiently on a target edge device with an ARM CPU. Which AWS service should they use?
⚠ Common exam trap
MLA-C01 often tests the distinction between hardware accelerators (Inferentia, Elastic Inference) and the compilation/optimization service (Neo) — candidates pick the chip when the question asks for a service to compile a model.
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
✓
SageMaker Neo
SageMaker Neo compiles trained models from frameworks like TensorFlow, PyTorch, and MXNet into optimized executables for specific target hardware, including ARM CPUs, Intel, and AWS Inferentia. It performs graph-level optimizations and generates a runtime that runs efficiently on the edge device.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SageMaker Debugger
Why it's wrong here
SageMaker Debugger captures tensors and analyses training jobs for convergence issues; it does not compile or optimise models for edge hardware. It is tempting because it sits within SageMaker's training toolchain, but the compilation task belongs to SageMaker Neo, which targets ARM CPUs.
- ✗
AWS Inferentia
Why it's wrong here
AWS Inferentia is a cloud inference accelerator chip for EC2 Inf1 instances, not a compiler for edge ARM CPUs. It is tempting because it accelerates TensorFlow inference, but the requirement is model compilation and optimisation for on-device ARM execution, which SageMaker Neo performs.
- ✓
SageMaker Neo
Why this is correct
SageMaker Neo compiles trained models for specific target hardware, including ARM CPUs, producing optimised executables that run efficiently on edge devices. It directly satisfies the stem's requirement to compile a TensorFlow model for an ARM-based edge target.
- ✗
Amazon Elastic Inference
Why it's wrong here
Elastic Inference attaches GPU-backed acceleration to SageMaker inference endpoints in the cloud; it neither compiles models nor targets ARM edge CPUs. It is tempting when seeking inference acceleration, but it is the correct choice for boosting cloud-hosted endpoint throughput, not for edge deployment.
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Same concept, more angles
1 more way this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which SageMaker feature compiles a trained model into an optimized binary for a specific hardware target (e.g., Intel, ARM, NVIDIA, or edge devices) to improve inference performance?
easy- A.SageMaker Model Monitor
- ✓ B.SageMaker Neo
- C.Amazon Elastic Inference
- D.SageMaker Clarify
Why B: SageMaker Neo is a model compilation service that optimizes models for specific hardware targets. Amazon Elastic Inference attaches GPU acceleration to endpoints, but does not compile models. Model Monitor monitors quality. SageMaker Clarify explains predictions.
JA
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.