MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is using SageMaker Ground Truth to create a labeled dataset for object detection. After the labeling job completes, the scientist notices that the output manifest file contains incorrect labels. What is the most efficient way to correct these labels?
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
✓
Create an incremental labeling job that includes only the mislabeled items.
SageMaker Ground Truth supports incremental labeling jobs that allow you to provide a new manifest with only mislabeled items, and the job will correct only those labels without re-labeling correctly labeled data. Option B is wrong because deleting and starting over is inefficient and loses all progress. Option C is wrong because the SageMaker console does not allow direct editing of manifest files; labels are fixed only through re-labeling. Option D is wrong because it would re-label the entire dataset, wasting time and resources.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create an incremental labeling job that includes only the mislabeled items.
Why this is correct
Efficiently corrects only errors.
- ✗
Delete the labeling job and start over with a different set of workers.
Why it's wrong here
Loses correctly labeled data.
- ✗
Use the SageMaker console to edit the incorrect labels directly in the manifest file.
Why it's wrong here
Manifest files are not editable via console.
- ✗
Create a new labeling job with the same dataset and manually verify all labels.
Why it's wrong here
Inefficient, re-labels all items.
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