Quick Answer
The answer is to use a private workforce with more workers, as this directly increases the parallelism available for labeling tasks. SageMaker Ground Truth distributes work items across the active workforce, so adding more annotators allows multiple images to be labeled simultaneously, raising overall throughput. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how workforce configuration impacts labeling job performance, often appearing as a scenario where throughput is bottlenecked by concurrency rather than annotation speed. A common trap is to assume reducing task complexity or increasing time per image helps, but those actually slow throughput; the key is scaling human resources. Remember the mnemonic "More hands, faster lands" to recall that throughput in Ground Truth scales with the number of parallel workers in your private workforce.
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 company is using SageMaker Ground Truth to label images for a computer vision model. After launching the labeling job, they notice that the labeling throughput is lower than expected. What should they do to increase throughput?
Use a private workforce with more workers.
More workers increase labeling parallelism and throughput.
Change the labeling task to use a single annotator per image.
Why wrong: Single annotator may be slower; consensus requires multiple.
Reduce the number of workers assigned to each task.
Why wrong: Fewer workers reduce throughput.
Increase the time allowed for each labeling task.
Why wrong: More time per task reduces throughput.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use a private workforce with more workers.
Option D is correct because using a private workforce with more workers increases parallelism. Option A is incorrect because reducing the number of workers decreases throughput. Option B is incorrect because using a single annotator per task may not speed up labeling. Option C is incorrect because increasing task time per image slows down throughput.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Use a private workforce with more workers.
Why this is correct
More workers increase labeling parallelism and throughput.
Related concept
Read the scenario before looking for a memorised answer.
Change the labeling task to use a single annotator per image.
Why it's wrong here
Single annotator may be slower; consensus requires multiple.
Reduce the number of workers assigned to each task.
Why it's wrong here
Fewer workers reduce throughput.
Increase the time allowed for each labeling task.
Why it's wrong here
More time per task reduces throughput.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
The correct answer is: Use a private workforce with more workers. — Option D is correct because using a private workforce with more workers increases parallelism. Option A is incorrect because reducing the number of workers decreases throughput. Option B is incorrect because using a single annotator per task may not speed up labeling. Option C is incorrect because increasing task time per image slows down throughput.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 2026
This MLS-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 MLS-C01 exam.
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