AI-102 Implement computer vision solutions Practice Question
You are building an Azure AI Vision solution that analyzes live video from a camera mounted on a delivery truck. The solution must read street signs in real time and return the recognized text with bounding box coordinates. You need to minimize latency and cost. Which Azure AI Vision feature should you use?
⚠ Common exam trap
The trap here is assuming that the Read API is always the best OCR choice, when its asynchronous, document-oriented design makes it unsuitable for real-time video frames.
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
✓
Optical character recognition (OCR) synchronous API
The synchronous OCR API is the correct choice because it extracts printed text with bounding boxes in a single call, which suits low-latency, real-time video frame analysis. The Read API and Document Intelligence are better for documents and asynchronous processing, and Custom Vision detects objects rather than reading text. Using synchronous OCR minimizes latency and cost for live street sign recognition.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Azure AI Document Intelligence prebuilt-read model
Why it's wrong here
Document Intelligence is built for extracting structured data from documents such as invoices and forms. It can read text, but it is not optimized for live video frames and lacks the low-latency synchronous path needed for real-time street sign recognition. Using it here would add unnecessary overhead and cost without providing the immediate response required.
- ✗
Custom Vision object detection model
Why it's wrong here
Custom Vision object detection identifies and localizes objects you have labeled, such as specific signs or logos, but it does not perform general text recognition. It cannot read arbitrary street sign text or return the characters and words. To recognize text, you need an OCR or Read capability, not an object detection model trained on custom classes.
- ✗
Read API with asynchronous processing
Why it's wrong here
The Read API is optimized for large documents and can process them asynchronously, but it introduces polling and is not intended for low-latency, frame-by-frame video analysis. For live street sign reading, the asynchronous workflow would add delay and complexity. It does return text and bounding boxes, but its design targets batch document scenarios rather than real-time camera feeds.
- ✓
Optical character recognition (OCR) synchronous API
Why this is correct
The synchronous OCR API is designed for near real-time, single-image text extraction and returns lines and words with bounding box coordinates. It is ideal for live video frames because you can call it per frame with low latency and pay only for the images you submit. It supports printed text in many languages and gives the positional data required to overlay results on the video.
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Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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