MLA-C01 ML Model Development Practice Question
A data scientist is training an object detection model using SageMaker built-in Object Detection algorithm. They want to visualize the bounding boxes on validation images after training. Which approach should they use?
⚠ Common exam trap
MLA-C01 often tests whether candidates assume built-in algorithms include visualization tooling, so they pick Debugger, Clarify, or Model Monitor instead of recognizing that custom inference code is required.
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
✓
Write a custom inference script that saves images with bounding boxes
SageMaker's built-in Object Detection algorithm outputs predictions in JSON (bounding boxes, class labels, scores) via inference; to visualize boxes on validation images, you must write a custom inference script that reads those predictions and draws rectangles on the images. No built-in visualization step exists in the algorithm itself. The other options address debugging, monitoring, or explainability, not bounding-box rendering.
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 Debugger to capture output tensors
Why it's wrong here
Debugger captures tensors and statistics during training for anomaly detection, and its output is not rendered as annotated validation images. It is tempting because it hooks into the training container, but bounding-box overlays require running inference and drawing predictions onto the images, which Debugger does not perform.
- ✓
Write a custom inference script that saves images with bounding boxes
Why this is correct
The built-in Object Detection algorithm emits bounding box coordinates as JSON output, not annotated images. A custom inference script must parse those predictions and draw boxes onto the validation images, since no built-in visualisation step exists.
- ✗
Enable SageMaker Model Monitor
Why it's wrong here
Model Monitor detects data drift and quality deviations on endpoints in production, comparing live traffic against a baseline. It is tempting because it analyses model behaviour, but it produces CloudWatch metrics and reports rather than annotated validation images, and it operates post-deployment, not on training output.
- ✗
Use SageMaker Clarify
Why it's wrong here
Clarify computes bias metrics and feature attributions for explainability, emitting statistical reports rather than rendered images. It is tempting because it inspects model behaviour on data, but it has no bounding-box rendering capability; visualising predictions on validation images requires running inference and drawing the detected boxes.
Go deeper
Related to this question
About these practice questions
This MLA-C01 question is part of Courseiva's 665-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.