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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 automatically selects the most informative unlabeled images for human labelling, so annotators spend effort only on samples that most improve the model. This directly minimises labelling cost while sustaining high accuracy, satisfying the stem's requirement to cut expense without sacrificing quality on the multi-class image task.
- ✗
Annotation consolidation
Why it's wrong here
Annotation consolidation combines multiple workers' responses into a single label, which raises accuracy but does not lower the volume of human-labelled images. It is tempting because it directly improves label quality, and would be correct when several annotators label each item and their outputs must be merged.
- ✗
Data labeling workforce management
Why it's wrong here
Workforce management configures and monitors private, vendor, or Mechanical Turk work teams; it does not itself reduce per-item labelling cost. It is tempting because it governs who labels data, and would be correct when selecting or managing a labelling workforce rather than automating labelling to cut expense.
- ✗
Consolidated labeling
Why it's wrong here
Consolidated labeling merges outputs from multiple workers into one label per item; it does not reduce the number of items sent for human review. It is tempting because it improves label quality, and would be correct when combining several annotators' results, not when cutting cost through automated pre-labelling.
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