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MLA-C01 Practice Question: A data engineer is using Amazon SageMaker Ground…
A data engineer is using Amazon SageMaker Ground Truth to create a labeled dataset for an object detection task. The dataset contains millions of images, and the labeling budget is limited. Which approach can reduce labeling costs while maintaining high model accuracy?
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
✓
Enable active learning in Ground Truth to automatically select a subset of images for human labeling
Active learning in Ground Truth selects the most informative samples (e.g., uncertain predictions) for human labeling, reducing the number of labels needed while maximizing model improvement. This is a built-in feature.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable active learning in Ground Truth to automatically select a subset of images for human labeling
Why this is correct
Active learning iteratively selects the most valuable samples for human labeling, reducing cost while maintaining model performance.
- ✗
Label 100% of the images using a pre-built worker template to ensure accuracy
Why it's wrong here
Labeling all images is unnecessary and expensive; active learning can achieve similar accuracy with fewer labels.
- ✗
Use Amazon SageMaker Data Wrangler to annotate images
Why it's wrong here
Data Wrangler is for tabular and structured data preparation, not image annotation.
- ✗
Use automated labeling with a pre-trained model for all images and skip human review
Why it's wrong here
Automated labeling may introduce errors, and without human verification, the quality may be insufficient for training.
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