AI-102 Implement computer vision solutions Practice Question
You are a senior AI engineer at a manufacturing company. The company has a production line that uses cameras to capture images of assembled products. The current system uses a set of rule-based heuristics to detect defects, but it has high false-positive rates. You have been tasked to design a new computer vision solution using Microsoft Azure AI services. The solution must:
- Detect defects such as scratches, dents, and misalignments in real-time as products move on the conveyor belt (frame rate of 30 fps). - Support continuous learning: when a new defect type is discovered, the model should be updated without retraining the entire model from scratch. - Operate with low latency (<100 ms per inference) to keep up with the production speed. - Use only fully managed services (no custom containers or edge devices). - The factory network has limited internet bandwidth, so the solution must minimize data transfer.
Which approach should you recommend?
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
✓
Use Azure AI Vision Spatial Analysis to detect defects in real-time by analyzing video feeds
Azure AI Vision Spatial Analysis is designed for analyzing human activities and movements, not for detecting product defects. It typically runs as a container on edge devices, contradicting the requirement for fully managed services. Using Azure AI Custom Vision as a managed service (without exporting to a container) would meet the requirements of real-time defect detection, continuous learning, and low latency, but none of the options provide this. Therefore, no option is correct.
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 Vision Spatial Analysis to detect defects in real-time by analyzing video feeds
Why this is correct
Correct. Azure AI Vision Spatial Analysis is a fully managed service that meets all requirements: real-time video analysis, low latency, and continuous learning via model updates. It minimizes data transfer by processing video at the edge (if needed) or in the cloud.
- ✗
Use Azure AI Video Indexer to index and search for defects in recorded videos
Why it's wrong here
Video Indexer is not real-time and adds latency.
- ✗
Use Azure AI Document Intelligence to analyze images of products
Why it's wrong here
Document Intelligence is for documents, not manufacturing defects.
- ✗
Train an object detection model using Azure AI Custom Vision, export it as a Docker container, and deploy it on an on-premises server with GPU
Why it's wrong here
Incorrect. Although Custom Vision can detect defects, exporting it as a Docker container and deploying on-premises violates the requirement to use only fully managed services with no custom containers or edge devices.
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This AI-102 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-102 exam.