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AIF-C01 Practice Question: Using Amazon SageMaker Ground Truth to create…
A company is using Amazon SageMaker Ground Truth to create labeled datasets for a computer vision model. The dataset contains images of people in public places. The company must comply with data privacy regulations that require explicit consent for using images of individuals. The company has a privacy team that reviews the images and provides consent lists. The ML team suspects that some images in the dataset might include individuals who have not consented. The company wants to ensure that only those images with consent are used for training. What should the company do?
⚠ Common exam trap
Test-takers frequently confuse privacy compliance with data anonymization (blurring faces) or bias detection, rather than recognizing that explicit consent requires a cross-referencing workflow against an external consent list, which is best orchestrated by a workflow service like SWF.
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 an Amazon Simple Workflow Service (SWF) workflow that cross-references image metadata with the consent list, and update the Ground Truth manifest to include only approved images.
It uses Amazon Simple Workflow Service (SWF) to orchestrate a cross-referencing workflow between image metadata and the consent list, then updates the SageMaker Ground Truth manifest to include only approved images. This ensures that only images with explicit consent are used for training, directly addressing the data privacy compliance requirement without altering or mislabeling the data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply a blur filter to all faces in the dataset using Amazon Rekognition before labeling.
Why it's wrong here
Blurring faces does not give consent; using the image may still be a violation.
- ✗
Use Amazon Rekognition to detect faces in all images and re-label those without consent as invalid.
Why it's wrong here
Rekognition detects faces but cannot determine consent status; re-labeling is manual and expensive.
- ✓
Create an Amazon Simple Workflow Service (SWF) workflow that cross-references image metadata with the consent list, and update the Ground Truth manifest to include only approved images.
Why this is correct
This creates an automated pipeline to filter approved images based on the consent list, using SWF for workflow orchestration.
- ✗
Use Amazon SageMaker Clarify to detect bias in the training data and exclude images of people.
Why it's wrong here
Clarify is for bias metrics, not consent management; excluding all people images may remove necessary data.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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