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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?
⚠ Common exam trap
MLA-C01 often tests the difference between active learning (selective human labeling to reduce cost) and automated labeling without review (which risks accuracy), causing candidates to choose full automation as a cost-saving measure.
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
Ground Truth active learning (automated data labeling) uses a machine learning model to identify the most informative unlabeled images — those where the model is least confident — and sends only those to human labelers. This reduces the number of human annotations required while maintaining model accuracy, directly addressing the limited labeling budget for millions of images. The other options either label everything (expensive), use the wrong tool, or skip human review entirely (risking accuracy).
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 uses model uncertainty sampling to route only the most informative images to human labellers, leaving confident predictions auto-labelled. This cuts the volume of paid human annotations across millions of images, satisfying the stem's limited-budget constraint while preserving accuracy on the object detection task.
- ✗
Label 100% of the images using a pre-built worker template to ensure accuracy
Why it's wrong here
Labelling every image exhausts the limited budget without exploiting Ground Truth's active-learning sampling, which selects only uncertain items for human annotation. Full labelling suits small datasets where cost is not constrained, but here it defeats the cost-reduction requirement while adding no accuracy benefit over targeted sampling.
- ✗
Use Amazon SageMaker Data Wrangler to annotate images
Why it's wrong here
Data Wrangler transforms and prepares tabular or image data for training; it does not provide a labelling workforce or annotation interface, so it cannot produce Ground Truth object-detection labels. It would be the right tool for feature engineering, cleansing or format conversion before the labelling job begins.
- ✗
Use automated labeling with a pre-trained model for all images and skip human review
Why it's wrong here
Skipping human review removes the verification that Ground Truth relies on to bound label noise, so accuracy degrades across millions of images. Automated labelling is genuinely useful for pre-labelling with a confidence threshold, where only low-confidence items are sent to humans, but not for the entire dataset.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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