- A
Pre-built worker templates
Why wrong: Templates define UI, not sample selection.
- B
Active learning
Active learning selects the most informative samples for labeling.
- C
Consolidated labeling
Why wrong: Consolidated labeling combines annotations, not sample selection.
- D
Automated data labeling
Why wrong: Automated labeling uses ML to label, but does not prioritize uncertain samples.
Question 43 of 1,000
easyMultiple ChoiceObjective-mapped
Reduce Labeling Costs with Active Learning in SageMaker Ground Truth
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data scientist needs to label a large dataset of product images for a classification model. They want to reduce labeling costs by prioritizing uncertain samples. Which Amazon SageMaker Ground Truth feature should they use?
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
✓
Active learning
Active learning in Amazon SageMaker Ground Truth automatically selects the most uncertain or informative samples from the unlabeled dataset and sends them to human annotators. This prioritization reduces labeling costs by focusing budget on the samples that will most improve model performance, rather than labeling all data indiscriminately.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Pre-built worker templates
Why it's wrong here
Templates define UI, not sample selection.
- ✓
Active learning
Why this is correct
Active learning selects the most informative samples for labeling.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Consolidated labeling
Why it's wrong here
Consolidated labeling combines annotations, not sample selection.
- ✗
Automated data labeling
Why it's wrong here
Automated labeling uses ML to label, but does not prioritize uncertain samples.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing 'active learning' with 'automated data labeling' — candidates often think automated labeling reduces costs by skipping humans entirely, but active learning specifically reduces costs by selectively using humans only on uncertain samples.
Detailed technical explanation
How to think about this question
Active learning in Ground Truth uses a selection strategy (e.g., uncertainty sampling or margin sampling) based on the model's prediction confidence scores. The system iteratively trains a model on a small labeled subset, then selects the unlabeled samples with the lowest confidence for human labeling, retraining after each batch. This is especially valuable in imbalanced datasets where the model is uncertain about rare classes, ensuring those critical samples are labeled first.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Active learning — Active learning in Amazon SageMaker Ground Truth automatically selects the most uncertain or informative samples from the unlabeled dataset and sends them to human annotators. This prioritization reduces labeling costs by focusing budget on the samples that will most improve model performance, rather than labeling all data indiscriminately.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.
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