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MLA-C01 Practice Question: Using Amazon SageMaker Ground Truth to create a…
A company is using Amazon SageMaker Ground Truth to create a labeled dataset for object detection in images. The team wants to minimize labeling costs while maintaining high accuracy. Which feature should they use to achieve this?
⚠ Common exam trap
MLA-C01 often tests the misconception that cost reduction in labeling comes from using more pre-labeled public data or fewer categories, when the intended answer is the active-learning-based automated labeling feature that specifically minimizes human labeling volume.
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
✓
Enable automated data labeling with active learning
Amazon SageMaker Ground Truth's automated data labeling uses active learning to train a model on the labels already produced by humans, then automatically labels the high-confidence data and only sends low-confidence samples to human labelers. This dramatically reduces the number of human-labeled items required, cutting costs while preserving accuracy because humans still validate uncertain cases. This is the specific cost-optimization feature designed for exactly this scenario.
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 a private workforce of internal employees
Why it's wrong here
A private workforce of internal employees gives higher accuracy and data privacy, but internal annotators still cost salary time and scale poorly. Ground Truth's cost minimisation relies on automatic labelling plus active learning, which sends only low-confidence images to human labellers.
- ✓
Enable automated data labeling with active learning
Why this is correct
Automated data labeling with active learning uses a model to label high-confidence objects and routes only uncertain images to human annotators, reducing the volume of manual labelling required while sustaining accuracy, thereby lowering overall Ground Truth labelling costs.
- ✗
Use a larger initial training set with pre-labeled public datasets
Why it's wrong here
Pre-labelled public datasets supply initial training data, but Ground Truth's cost reduction comes from automatic labelling and active learning, which label only uncertain samples. Public datasets may not match the target domain, and this adds no labelling-cost mechanism within Ground Truth itself.
- ✗
Reduce the number of label categories
Why it's wrong here
Fewer label categories reduces annotation effort per image but does not lower Ground Truth's labelling cost mechanism, and it can harm accuracy by collapsing distinct object classes. Automatic labelling with active learning is the feature that cuts human labelling volume while preserving accuracy.
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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.