The answer is to create a pre-labeling task where workers first identify if an object exists, then send only positive images for full labeling. This approach directly addresses the inefficiency of workers spending time on empty images by using a two-stage verification process: a quick binary classification (object present or not) filters out negatives, and only images with objects proceed to the detailed bounding box annotation. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this tests your understanding of SageMaker Ground Truth’s built-in task types, specifically the “verify” labeling mode for object detection, which optimizes cost by reducing unnecessary work. A common trap is assuming automated data labeling alone can filter empty images, but the exam emphasizes that a human-in-the-loop pre-filter is more reliable for edge cases. Memory tip: think “Verify first, annotate second” to remember the two-step cost-saving strategy.
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 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.
Ground Truth supports automated data labeling and can use a pre-built model to filter out images with no objects. However, the most effective way is to use a pre-labeling task with a machine learning model to automatically reject images without objects. Alternatively, using a 'verify' labeling task where workers only verify if objects exist can be efficient. The best option is to use a 'verify' task mode, which is available for object detection.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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
Static NAT maps one inside address to one outside address.
✗
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: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Static NAT maps one inside address to one outside address..
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. — Ground Truth supports automated data labeling and can use a pre-built model to filter out images with no objects. However, the most effective way is to use a pre-labeling task with a machine learning model to automatically reject images without objects. Alternatively, using a 'verify' labeling task where workers only verify if objects exist can be efficient. The best option is to use a 'verify' task mode, which is available for object detection.
What should I do if I get this MLS-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Question Discussion
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