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MLA-C01 Practice Question: A data scientist is using Amazon SageMaker Ground…
A data scientist is using Amazon SageMaker Ground Truth to create a labeled dataset for an object detection model. The dataset contains 1 million images, and the team wants to reduce labeling cost by labeling only the most informative samples. Which feature of Ground Truth should they use?
⚠ Common exam trap
MLA-C01 often tests the confusion between active learning (which samples to label) and automated data labeling (which samples to skip) — both reduce cost but via different mechanisms.
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
✓
Active learning
Ground Truth active learning automatically selects the most informative unlabeled samples for human labeling by using a model to score uncertainty, so the team labels far fewer than 1 million images while achieving similar model accuracy. It's designed exactly for cost reduction on large datasets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Active learning
Why this is correct
Active learning automatically selects unlabelled images where the model is least confident and sends only those to human labellers. Across a million images this concentrates annotation effort on informative samples, directly reducing labelling cost as the stem demands.
- ✗
Automated data labeling
Why it's wrong here
Automated data labelling uses active learning to label a subset automatically and sends only low-confidence items to humans, cutting cost. It is tempting because it reduces human effort, but the stem asks for selecting the most informative samples for labelling.
- ✗
Pre-built annotation worker UI
Why it's wrong here
The pre-built worker UI only changes how annotators draw boxes; it does not choose which images get labelled. It is tempting because it speeds annotation, but sample selection for cost reduction comes from active learning, not the interface.
- ✗
Consolidated labeling
Why it's wrong here
Consolidated labeling merges multiple annotations of the same object into one output; it does not select informative samples. It is tempting because it reduces cost by cutting redundant human review, and would be correct when several workers label identical items and you need agreement resolution, not active-learning selection.
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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.