AI-102 Implement agentic AI solutions Practice Question
A company is developing an agent that uses Azure AI Vision to analyze images uploaded by users. The agent must identify objects and read text in images. The team uses the Azure AI Vision API. During testing, the agent fails to read text from images with low contrast. What should the team do to improve optical character recognition (OCR) accuracy for such images?
⚠ Common exam trap
Many candidates assume Azure Custom Vision can be repurposed for OCR or that adjusting confidence thresholds can fix recognition accuracy, when in fact pre-processing the image is the standard approach to improve OCR results for low-quality inputs.
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
✓
Pre-process the image to adjust contrast and brightness before calling the OCR API.
Azure AI Vision's OCR API performs best on images with sufficient contrast and brightness. Pre-processing the image (e.g., using OpenCV or PIL to adjust contrast and brightness) enhances text visibility, directly improving OCR accuracy for low-contrast images without changing the API or training a custom model.
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 a different OCR API from Azure Cognitive Services.
Why it's wrong here
Switching OCR APIs does not address low-contrast input; the Read API already handles it, and alternative Cognitive Services OCR endpoints offer no contrast-specific preprocessing. It is tempting when accuracy plateaus, but the correct fix is image preprocessing or the Read model's enhanced contrast handling.
- ✓
Pre-process the image to adjust contrast and brightness before calling the OCR API.
Why this is correct
Low-contrast images degrade the pixel gradients OCR relies on to segment characters. Adjusting contrast and brightness before the API call restores that separation, improving recognition accuracy without retraining or changing the Azure AI Vision service.
- ✗
Train a custom OCR model using Azure Custom Vision.
Why it's wrong here
Custom Vision trains image classification and object detection models, not OCR text extractors, so it cannot produce a recognition model for this task. It is tempting because custom training improves accuracy on domain-specific visuals, and would be correct for labelling bespoke objects or product categories rather than reading characters.
- ✗
Increase the confidence threshold for text detection.
Why it's wrong here
Raising the confidence threshold discards detections the model already made, reducing recall rather than recovering text it never recognised in low-contrast regions. It is tempting because thresholds tune output precision, and would be correct when too many false positives appear in otherwise legible images.
Go deeper
Related to this question
About these practice questions
This AI-102 question is part of Courseiva's 761-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.