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MLA-C01 Data Preparation for Machine Learning Practice Question

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?

⚠ Common exam trap

Watch out — candidates often 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.

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

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.

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.

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