- A
Use a built-in transformation to convert from Ground Truth JSON to COCO after labeling
Why wrong: You can choose the output format directly.
- B
Use a pre-built AWS Lambda function to transform annotations to COCO
Why wrong: Not required; Ground Truth has native output format options.
- C
Write a custom SageMaker Processing script to convert the output to COCO
Why wrong: This adds unnecessary complexity.
- D
Select 'Object Detection' task type and specify 'COCO' as the output format in the labeling job configuration
Ground Truth supports COCO output for object detection tasks.
Question 749 of 1,000
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 uses Amazon SageMaker Ground Truth to create labeled datasets for object detection. The output must be in COCO format for downstream model training. How should the data preparation process be configured?
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
✓
Select 'Object Detection' task type and specify 'COCO' as the output format in the labeling job configuration
Option D is correct because Amazon SageMaker Ground Truth natively supports outputting object detection labeling jobs in COCO format. When you select 'Object Detection' as the task type, the labeling job configuration includes an option to specify 'COCO' as the output format, which automatically structures the labeled data into the required COCO JSON schema without any post-processing.
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 built-in transformation to convert from Ground Truth JSON to COCO after labeling
Why it's wrong here
You can choose the output format directly.
- ✗
Use a pre-built AWS Lambda function to transform annotations to COCO
Why it's wrong here
Not required; Ground Truth has native output format options.
- ✗
Write a custom SageMaker Processing script to convert the output to COCO
Why it's wrong here
This adds unnecessary complexity.
- ✓
Select 'Object Detection' task type and specify 'COCO' as the output format in the labeling job configuration
Why this is correct
Ground Truth supports COCO output for object detection tasks.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume post-processing is always required for format conversion, overlooking that Ground Truth can directly output COCO format when the correct task type and output format are selected in the labeling job configuration.
Trap categories for this question
Command / output trap
You can choose the output format directly.
Detailed technical explanation
How to think about this question
When you configure a Ground Truth labeling job for object detection and select COCO output, the service generates a single JSON file following the COCO annotation format, which includes fields like 'images', 'annotations', and 'categories'. This format is directly compatible with popular object detection frameworks such as Detectron2 and MMDetection, streamlining the pipeline from labeling to training. A subtle behavior is that if you choose the default 'Augmented Manifest' output, you must manually convert the bounding box coordinates from normalized to pixel values, whereas COCO output handles this automatically.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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?
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Select 'Object Detection' task type and specify 'COCO' as the output format in the labeling job configuration — Option D is correct because Amazon SageMaker Ground Truth natively supports outputting object detection labeling jobs in COCO format. When you select 'Object Detection' as the task type, the labeling job configuration includes an option to specify 'COCO' as the output format, which automatically structures the labeled data into the required COCO JSON schema without any post-processing.
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.
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Last reviewed: Jun 24, 2026
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