MLS-C01 Practice Question: Machine Learning Implementation and Operations
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?
⚠ Common exam trap
Watch out — candidates often confuse throughput with quality or accuracy, thinking that reducing workers or increasing time will improve speed, when in fact throughput is a direct function of parallel worker capacity.
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 a private workforce with more workers.
SageMaker Ground Truth labeling throughput is primarily limited by the number of workers available to process tasks. Using a private workforce allows you to directly control and scale the number of workers, which increases parallelism and overall throughput. Public or vendor workforces have fixed capacity and may not scale as quickly, so adding more private workers is the most effective way to boost throughput.
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 with more workers.
Why this is correct
More workers increase labeling parallelism and throughput.
- ✗
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.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.