- A
Use a private workforce instead of public.
Why wrong: Private workforce may be faster but does not address the empty image issue.
- B
Create a pre-labeling task where workers only identify if an object exists, then send only positive images for full labeling.
This two-stage approach reduces work on empty images.
- C
Use automated data labeling with a pre-trained model to filter empty images.
Why wrong: Automated labeling can create labels but not filter tasks; workers still see all images.
- D
Increase the number of workers per dataset object.
Why wrong: More workers per object increases cost and time.
Question 1,040 of 1,755
MLS-C01 Practice Question: Machine Learning Implementation and Operations
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 Amazon SageMaker Ground Truth to create a labeled dataset for object detection. The labeling job is taking longer than expected. The team notices that many workers are spending a lot of time on images with no objects. Which labeling strategy should they use to reduce costs and time?
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
✓
Create a pre-labeling task where workers only identify if an object exists, then send only positive images for full labeling.
Option B is correct because it introduces a two-stage labeling workflow: first, workers perform a quick binary classification to identify images containing objects, and only those positive images proceed to the expensive, time-consuming bounding box annotation. This directly reduces the cost and time spent on empty images, which is the root cause of the delay.
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 instead of public.
Why it's wrong here
Private workforce may be faster but does not address the empty image issue.
- ✓
Create a pre-labeling task where workers only identify if an object exists, then send only positive images for full labeling.
Why this is correct
This two-stage approach reduces work on empty images.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use automated data labeling with a pre-trained model to filter empty images.
Why it's wrong here
Automated labeling can create labels but not filter tasks; workers still see all images.
- ✗
Increase the number of workers per dataset object.
Why it's wrong here
More workers per object increases cost and time.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume 'automated data labeling' (Option C) is the fastest solution, but they overlook that it requires a pre-trained model and labeled data to start, making it impractical for a new labeling project where the goal is to create the initial labeled dataset.
Detailed technical explanation
How to think about this question
This two-stage approach is a form of 'pre-filtering' or 'pre-labeling' that leverages a simple classification task (object existence) before the more complex object detection task. In Amazon SageMaker Ground Truth, you can chain labeling jobs using a custom workflow or AWS Step Functions, where the output of the first job (e.g., a JSON line with a 'no-object' flag) triggers a conditional second job only for positive images. This mirrors real-world scenarios like satellite imagery analysis, where vast areas contain no objects of interest, and pre-filtering can reduce labeling costs by 80% or more.
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.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create a pre-labeling task where workers only identify if an object exists, then send only positive images for full labeling. — Option B is correct because it introduces a two-stage labeling workflow: first, workers perform a quick binary classification to identify images containing objects, and only those positive images proceed to the expensive, time-consuming bounding box annotation. This directly reduces the cost and time spent on empty images, which is the root cause of the delay.
What should I do if I get this MLS-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
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