- A
Enable active learning to select the most informative samples
Active learning reduces the number of samples needed for labeling.
- B
Use a pre-built annotation workflow for image classification
Pre-built workflows streamline the labeling process, reducing time and cost.
- C
Use a private workforce with domain expertise
A private workforce may be more efficient and accurate, reducing overall cost for complex tasks.
- D
Use a public workforce (Mechanical Turk) for all labeling
Why wrong: Public workforce may be cheaper per label but can be less accurate, leading to rework costs; not necessarily cost-saving.
- E
Label all data manually without automation
Why wrong: Manual labeling without active learning or pre-built workflows is more expensive.
Question 954 of 1,000
hardMultiple SelectObjective-mapped
Reducing 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 team is using Amazon SageMaker Ground Truth to build a labeled dataset for a multi-class classification task. They have a small budget and want to reduce labeling costs. Which THREE features or strategies should they use? (Select THREE.)
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 to select the most informative samples
Active learning in SageMaker Ground Truth automatically selects the most informative or uncertain samples from the unlabeled dataset to be sent for human labeling. By focusing labeling effort on these high-value data points, the team can achieve a high-quality model with significantly fewer labeled examples, directly reducing labeling costs.
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.
- ✓
Enable active learning to select the most informative samples
Why this is correct
Active learning reduces the number of samples needed for labeling.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use a pre-built annotation workflow for image classification
Why this is correct
Pre-built workflows streamline the labeling process, reducing time and cost.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use a private workforce with domain expertise
Why this is correct
A private workforce may be more efficient and accurate, reducing overall cost for complex tasks.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a public workforce (Mechanical Turk) for all labeling
Why it's wrong here
Public workforce may be cheaper per label but can be less accurate, leading to rework costs; not necessarily cost-saving.
- ✗
Label all data manually without automation
Why it's wrong here
Manual labeling without active learning or pre-built workflows is more expensive.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume using a public workforce (Mechanical Turk) is always cheaper, but the question specifically asks for cost-reduction strategies, and a private workforce with domain expertise reduces rework and per-label costs, while active learning and pre-built workflows directly minimize the number of labels needed.
Detailed technical explanation
How to think about this question
SageMaker Ground Truth's active learning uses a trained model to compute prediction confidence scores; samples with low confidence (e.g., softmax probabilities near 0.5) are flagged for human review. The system can also use a 'consensus' mechanism where multiple annotators label the same sample, but active learning reduces the total number of samples needing such consensus. In practice, for a multi-class image classification task with 10,000 images, active learning can cut labeling costs by 50-80% by only labeling the most ambiguous 2,000 images.
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 active learning to select the most informative samples — Active learning in SageMaker Ground Truth automatically selects the most informative or uncertain samples from the unlabeled dataset to be sent for human labeling. By focusing labeling effort on these high-value data points, the team can achieve a high-quality model with significantly fewer labeled examples, directly reducing labeling costs.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
2 more ways this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company is using Amazon SageMaker Ground Truth to create a labeled dataset for object detection in images. The team wants to minimize labeling costs while maintaining high accuracy. Which feature should they use to achieve this?
easy- A.Use a private workforce of internal employees
- ✓ B.Enable automated data labeling with active learning
- C.Use a larger initial training set with pre-labeled public datasets
- D.Reduce the number of label categories
Why B: Active learning in Ground Truth automatically selects the most uncertain or informative samples from the unlabeled pool and sends them to human labelers, reducing the total number of images that need manual labeling.
Variation 2. 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?
hard- A.Use a private workforce to label all data manually
- B.Set the labeling job to random sampling of data
- C.Configure the labeling job to use only bounding box annotations
- ✓ D.Enable automated data labeling with a pre-trained model to select uncertain samples
Why D: 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.
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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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