easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: An ML team wants to use Amazon SageMaker Ground…
An ML team wants to use Amazon SageMaker Ground Truth to create a labeled dataset for a multi-class image classification task. They have a large set of unlabeled images and want to minimize labeling costs while maintaining high accuracy. Which Ground Truth feature should they enable?
⚠ Common exam trap
Test-takers frequently confuse 'annotation consolidation' (a post-labeling quality step) with a cost-reduction feature, or think that workforce management alone reduces costs, when in fact active learning is the specific feature designed to minimize the number of labels 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
✓
Active learning
Active learning in SageMaker Ground Truth automatically selects the most informative unlabeled images for human labeling, reducing the total number of labels needed while maintaining model accuracy. By iteratively training a model on a small labeled subset and then using that model to identify uncertain predictions, the system focuses labeling effort on the data that will most improve the model, directly minimizing labeling costs.
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 the most uncertain or informative samples for labeling, minimizing cost while maximizing model improvement.
- ✗
Annotation consolidation
Why it's wrong here
Annotation consolidation merges multiple annotations into one, but does not actively reduce the number of images to label.
- ✗
Data labeling workforce management
Why it's wrong here
Workforce management helps assign tasks to workers but does not select which data to label.
- ✗
Consolidated labeling
Why it's wrong here
Consolidated labeling refers to combining annotations from multiple workers, not reducing the number of labels needed.
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