AI-900 Practice Question: Describe features of computer vision workloads on Azure
A parking lot management company uses security cameras to monitor vehicles. They need to both detect the presence of license plates in an image and read the alphanumeric characters on those plates. Which Azure Computer Vision capability should they use to achieve both requirements?
⚠ Common exam trap
Test-takers frequently confuse object detection (which can locate a license plate) with OCR (which can both locate and read the text), leading them to pick Custom Vision or Image Analysis instead of the Read API.
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) - Read API
(OCR - Read API) is correct because Azure's Read API is specifically designed to both detect the presence of text (including license plates) in an image and extract the alphanumeric characters from that text. This meets both requirements—detecting the plate and reading its characters—in a single call, using deep-learning-based recognition models optimized for printed and handwritten text.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Image Analysis (describe image and detect objects)
Why it's wrong here
Image Analysis for describe image and object detection identifies and labels visual content (e.g., "car", "license plate") and creates natural-language captions, but its output is object classes and bounding boxes rather than transcribed text. It cannot reliably isolate and extract the actual alphanumeric characters stamped on a plate; that requires a dedicated OCR engine. Without reading the characters, the company would not know the plate number.
- ✓
Optical Character Recognition (OCR) - Read API
Why this is correct
The OCR Read API detects text regions in an image, performs character recognition, and returns extracted strings along with word/line bounding boxes, which makes it ideal for both locating and reading license plates. Because license plates contain printed letters and digits, OCR directly produces the plate number as machine-readable text without needing a custom model. In Azure AI Vision, Read is the recommended OCR solution for images with embedded text.
- ✗
Face API
Why it's wrong here
Face API is designed exclusively for detecting and analyzing human faces, including face landmarks, attributes like age and emotion, and person identification/verification. It has no object detection for vehicles and no text extraction capability for license plates. Feeding it a parking-lot image will either find no human face or ignore the plate region entirely, so it is entirely unsuitable for this task.
- ✗
Custom Vision (object detection)
Why it's wrong here
Custom Vision object detection can be trained to draw bounding boxes around license plates and classify them as "license plate" objects, but it does not read or output the characters inside those boxes. The model's output is a categorical label plus location coordinates and confidence, not a text string. To obtain the actual plate characters from a detected region, you must add an OCR step, so Custom Vision alone cannot fulfill the requirement.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
OCR
Optical Character Recognition is technology that converts images of text, like scanned documents or photos, into machine-readable text data.
Key term
Computer vision
Computer vision is a field of artificial intelligence that enables computers to interpret and make decisions based on visual data from the world, such as images and videos.
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