MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is using Amazon SageMaker Ground Truth to create a labeled dataset for object detection. The team has limited budget and wants to minimize labeling costs while ensuring high-quality labels. Which approach is MOST cost-effective?
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
✓
Use active learning to automatically label high-confidence data and send only uncertain data to a private workforce.
Active learning uses the model to automatically label high-confidence data points, while only sending low-confidence or uncertain data to a private workforce for human labeling. This significantly reduces the number of data points requiring manual labeling, thereby minimizing costs while still ensuring high-quality labels through expert review of challenging cases. Option A is incorrect because using only a private workforce for all data is expensive due to the high cost of domain experts. Option B is incorrect because using a public workforce with three workers per data point increases labeling costs without necessarily guaranteeing higher quality than a focused approach. Option D is incorrect because relying solely on automated labeling without human review can introduce errors and reduce label quality, especially for uncertain cases.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use only a private workforce of domain experts to label all data.
Why it's wrong here
Expert labeling is expensive.
- ✗
Use a public workforce and have each data point labeled by three workers.
Why it's wrong here
Multiple labels increase cost.
- ✓
Use active learning to automatically label high-confidence data and send only uncertain data to a private workforce.
Why this is correct
Active learning reduces labeling cost while ensuring quality.
- ✗
Use the built-in automated labeling feature without human review.
Why it's wrong here
Automated labeling may have low accuracy.
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