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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?
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 offers active learning, which automatically selects the most informative samples to label, reducing cost. Option A is correct. Options B, C, and D do not provide automatic sample selection for labeling.
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 selects samples where the model is uncertain, maximizing labeling efficiency.
- ✗
Automated data labeling
Why it's wrong here
Automated labeling uses a model to label data automatically, but does not select informative samples.
- ✗
Pre-built annotation worker UI
Why it's wrong here
This is the interface for workers, not a sample selection method.
- ✗
Consolidated labeling
Why it's wrong here
Consolidation aggregates labels from multiple workers; does not select samples.
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