- A
Use a private workforce to label all data manually
Why wrong: Manual labeling of all data is costly and does not use active learning.
- B
Set the labeling job to random sampling of data
Why wrong: Random sampling is not active learning; it does not prioritize uncertain samples.
- C
Configure the labeling job to use only bounding box annotations
Why wrong: Annotation type does not relate to active learning.
- D
Enable automated data labeling with a pre-trained model to select uncertain samples
This is active learning: the model automatically selects data that needs human labeling based on uncertainty.
Question 128 of 1,000
hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A company uses Amazon SageMaker Ground Truth to…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 company uses Amazon SageMaker Ground Truth to label a dataset for object detection. To reduce labeling costs, they want to use active learning. Which configuration should they set up in Ground Truth?
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 automated data labeling with a pre-trained model to select uncertain samples
Active learning in Amazon SageMaker Ground Truth reduces labeling costs by automatically selecting the most uncertain or informative data samples for human review, rather than labeling all data. Option D correctly configures automated data labeling with a pre-trained model to select uncertain samples, which is the core mechanism of active learning in Ground Truth.
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.
- ✗
Use a private workforce to label all data manually
Why it's wrong here
Manual labeling of all data is costly and does not use active learning.
- ✗
Set the labeling job to random sampling of data
Why it's wrong here
Random sampling is not active learning; it does not prioritize uncertain samples.
- ✗
Configure the labeling job to use only bounding box annotations
Why it's wrong here
Annotation type does not relate to active learning.
- ✓
Enable automated data labeling with a pre-trained model to select uncertain samples
Why this is correct
This is active learning: the model automatically selects data that needs human labeling based on uncertainty.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse active learning with simply using a pre-trained model for inference (like option C's annotation type) or with random sampling (option B), missing that active learning specifically requires a feedback loop to select uncertain samples for human review.
Detailed technical explanation
How to think about this question
Active learning in Ground Truth uses a pre-trained model to infer labels on unlabeled data and calculates a confidence score; samples with low confidence (high uncertainty) are automatically sent to the human workforce for labeling, while high-confidence samples are accepted without human review. This iterative process, known as 'automated data labeling,' can reduce labeling costs by up to 70% in object detection tasks by focusing human effort on the most ambiguous examples. The underlying mechanism relies on a 'selection strategy' (e.g., least confidence or margin sampling) to determine which samples to route for manual annotation.
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: Enable automated data labeling with a pre-trained model to select uncertain samples — Active learning in Amazon SageMaker Ground Truth reduces labeling costs by automatically selecting the most uncertain or informative data samples for human review, rather than labeling all data. Option D correctly configures automated data labeling with a pre-trained model to select uncertain samples, which is the core mechanism of active learning in Ground Truth.
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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