AI-102 Implement computer vision solutions Practice Question
You have a computer vision solution that analyzes security camera feeds to detect people and vehicles. The solution uses Azure AI Vision Spatial Analysis. You need to ensure compliance with privacy regulations by blurring detected faces. Which feature should you enable?
⚠ Common exam trap
Candidates often confuse Azure AI Video Indexer's face redaction capabilities with Spatial Analysis's real-time face blurring, or assume that a separate SDK or service is required for face blurring when it is actually a built-in configuration option in Spatial Analysis.
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
✓
Enable face blurring in the Spatial Analysis configuration
Azure AI Vision Spatial Analysis includes a built-in face blurring feature that can be enabled directly in the Spatial Analysis configuration. This allows you to automatically blur detected faces in the video feed at the edge or in the cloud, ensuring compliance with privacy regulations without requiring additional services or post-processing steps.
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 Azure AI Content Safety to filter faces
Why it's wrong here
Content Safety classifies and filters harmful text or imagery; it does not detect faces within Spatial Analysis video streams, so no blurring coordinates are produced. It is tempting because it is a privacy-adjacent Azure AI service, and it would be correct for moderating offensive content rather than redacting individuals.
- ✗
Post-process frames with Azure AI Face client SDK
Why it's wrong here
Post-processing frames with the Azure AI Face client SDK would require sending each frame to the Face API for detection and blurring, introducing latency that breaks the real-time requirement of live security camera feeds. The temptation arises because the Face SDK is explicitly designed for face detection and redaction, and in offline or batch-processing scenarios—such as blurring faces in recorded video archives—it would be the correct choice. However, Spatial Analysis already includes built-in face blurring as a native privacy feature, eliminating the need for an external SDK call.
- ✗
Enable face detection and redact faces using Azure AI Video Indexer
Why it's wrong here
Video Indexer is a separate post-processing service for uploaded media, not an in-stream Spatial Analysis operation, so it cannot blur faces in live camera feeds. It is tempting because it genuinely offers face redaction, and it would be correct for batch privacy editing of recorded video archives.
- ✓
Enable face blurring in the Spatial Analysis configuration
Why this is correct
Face blurring in the Spatial Analysis configuration redacts detected faces in the video stream before storage or transmission, satisfying the stem's privacy compliance requirement. It operates within the spatial analysis pipeline itself, unlike separate Face service redaction applied after processing.
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