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AI-900 Practice Question: Describe features of computer vision workloads on Azure

A government agency needs to digitize thousands of handwritten application forms so that the text can be searched and processed. Which Azure Computer Vision capability should they use?

⚠ Common exam trap

Watch out — candidates often confuse image classification (which categorizes the whole image) with OCR, not realizing that only OCR extracts actual text content for searchability.

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 (Read API)

Optical Character Recognition (Read API), because the agency needs to extract printed or handwritten text from images of application forms and make it searchable and processable. The Read API is specifically designed for this purpose, handling both printed and handwritten text, and is part of Azure Computer Vision's OCR capabilities.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Object detection

    Why it's wrong here

    Object detection models used by Azure Computer Vision are trained to identify and localize objects like cars, people, furniture, or animals within an image, drawing bounding boxes around each detected item. These models classify regional visual features into semantic object categories, but they lack any character-level recognition capability. Thus, they cannot read or transcribe the handwritten words on a document, making the service irrelevant for text digitization.

  • Optical Character Recognition (Read API)

    Why this is correct

    The Read API in Azure Cognitive Services is an optical character recognition (OCR) service specifically designed to extract printed and handwritten text from images, PDFs, and scanned documents. It uses deep neural networks to identify individual words and lines, returning the text content along with bounding boxes and a confidence score for each element. For a government agency needing to digitize thousands of handwritten notes, the Read API is the correct choice because it transcribes handwriting into machine-readable text.

  • Image classification

    Why it's wrong here

    Image classification assigns a single label or set of tags to an entire image based on its dominant semantic content, such as 'scanned-document', 'letter', or 'receipt'. It relies on global visual features like color histograms and object presence, but it does not segment the image into text regions or decode the pixels that represent individual glyphs. As a result, it can categorize an image but cannot extract the actual handwritten text, so it cannot fulfill the digitization requirement.

  • Face detection

    Why it's wrong here

    Face detection in Azure Computer Vision scans an image for human faces and returns bounding boxes plus attributes such as age, emotion, or glasses. It works by analyzing facial landmarks and patterns, not by interpreting characters, words, or strokes. Since handwritten notes contain no faces, this service would produce no useful text output and cannot digitize the content.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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