AI-103 Computer Vision Practice Question
Your application analyzes high-resolution engineering blueprints using Azure AI Vision Read API. Users report that small text annotations are occasionally missed. How can you optimize the input image or API configuration to improve recognition of small text?
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
✓
Ensure the image is submitted at full resolution without lossy compression, and verify contrast.
For high-resolution images or very small text, ensuring the image is clear and within supported size limits, or preprocessing to enhance contrast and scale, is crucial. Additionally, Image Analysis 4.0 Read handles high-res documents efficiently when passed directly.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Convert the blueprint to grayscale and reduce color depth to 1-bit monochrome.
Why it's wrong here
Aggressive 1-bit thresholding often destroys fine lines and small text characters on blueprints.
- ✗
Switch from Read API to Tagging API.
Why it's wrong here
Tagging API does not perform OCR.
- ✓
Ensure the image is submitted at full resolution without lossy compression, and verify contrast.
Why this is correct
Preserving high resolution and sharp contrast ensures small text features are retained for the OCR model.
- ✗
Downsample the image resolution to under 1000x1000 pixels before submission.
Why it's wrong here
Downsampling reduces detail and hurts small text recognition.
About these practice questions
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
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