MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is using Amazon SageMaker Ground Truth to create labeled datasets for a text classification task. The labeling job uses a private workforce of 10 annotators. After labeling 10,000 items, the quality of labels is inconsistent. Which approach will MOST effectively improve labeling consistency?
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
✓
Configure the labeling job to use annotation consolidation with majority voting and require multiple annotations per item.
Requiring multiple annotations per item and using annotation consolidation with majority voting directly improves labeling consistency by reducing individual annotator bias and ensuring that the final label is based on consensus. Option A is incorrect because removing annotations from low-agreement annotators after the job completes does not improve the quality of labels already assigned; it may also discard valid data. Option B is incorrect because simply increasing the number of annotators does not guarantee consistency; it may introduce more variance without a consolidation mechanism. Option D is incorrect because active learning is used to select which items to label, not to improve the consistency of labeling; it does not address the inconsistency in the labeling process itself.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove annotations from annotators with low agreement after the job completes.
Why it's wrong here
Removing annotations reduces dataset size and may discard valid labels.
- ✗
Increase the number of annotators to 20 to average out inconsistencies.
Why it's wrong here
More annotators may still produce inconsistent labels without a consensus mechanism.
- ✓
Configure the labeling job to use annotation consolidation with majority voting and require multiple annotations per item.
Why this is correct
Consensus from multiple annotators and majority voting yields more consistent labels.
- ✗
Use active learning to automatically label the most confident samples and only send uncertain ones to annotators.
Why it's wrong here
Active learning improves labeling efficiency but does not improve annotator consistency.
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