easyMultiple SelectObjective-mapped
MLA-C01 Practice Question: Deploy its trained model to edge devices such as…
A company wants to deploy its trained model to edge devices such as cameras and IoT devices. The model must run efficiently with low latency and minimal memory footprint. Which THREE actions should the company take to prepare the model for edge deployment? (Choose THREE.)
⚠ Common exam trap
AWS often tests the misconception that edge deployment can rely on cloud endpoints for inference, but the correct approach is to optimize and run the model locally on the device to achieve low latency and offline operation.
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 Edge Manager to package and manage the model on devices.
SageMaker Edge Manager is purpose-built to package, optimize, and manage machine learning models on edge devices. It provides model packaging, runtime monitoring, and over-the-air updates, ensuring the model runs efficiently with low latency and minimal memory footprint on resource-constrained hardware like cameras and IoT devices.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use SageMaker Edge Manager to package and manage the model on devices.
Why this is correct
Edge Manager provides tools for model packaging, deployment, and monitoring on edge.
- ✓
Quantize the model to reduce precision and memory footprint.
Why this is correct
Quantization reduces model size and speeds up inference on edge.
- ✗
Increase the model's complexity to improve accuracy on edge devices.
Why it's wrong here
Increasing model complexity adds layers and parameters, which directly increases inference latency and memory consumption, contradicting the stem’s requirement for low latency and minimal memory footprint on edge devices. This option is tempting because deeper models often yield higher accuracy on powerful hardware, and would be correct for a cloud deployment where compute resources are abundant and latency constraints are relaxed.
- ✓
Use SageMaker Neo to compile the model for the target edge hardware.
Why this is correct
Neo optimizes models for specific hardware for better performance.
- ✗
Deploy the model directly as a SageMaker endpoint and have the edge devices call it over the internet.
Why it's wrong here
Edge deployment means running locally, not relying on cloud inference.
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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.