Quick Answer
The answer is Object Detection and Read (OCR). Object Detection is correct because it uses pre-trained models to identify and locate multiple objects within an image, returning bounding box coordinates and labels, which directly fulfills the requirement to detect objects. Read (OCR) is correct because it extracts printed text from images, converting it into machine-readable characters, meeting the need to read printed text. On the Microsoft Azure AI Engineer Associate AI-102 exam, this question tests your ability to distinguish between Azure AI Vision’s pre-built image analysis capabilities—specifically, that Object Detection handles spatial object localization while Read handles text extraction, not the Optical Character Recognition (OCR) legacy API. A common trap is confusing the older OCR API with the newer Read API, which is optimized for printed text. Memory tip: “Detect the box, Read the text”—Object Detection gives you boxes around objects, Read gives you the words inside.
This AI-102 practice question tests your understanding of plan and manage an azure ai solution. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
You are developing an Azure AI solution that uses pre-built models from Azure AI Vision to analyze images. The solution must be able to detect objects and read printed text. Which TWO capabilities should you use?
OCR (legacy)
Why wrong: Legacy API; Read is recommended.
Facial detection
Why wrong: Not required for the given requirements.
Object detection
Detects objects in images.
Image tagging
Why wrong: Tagging assigns keywords, not object bounding boxes.
Read (OCR)
Extracts printed text from images.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Object detection
Option C is correct because Azure AI Vision's Object Detection capability identifies and locates objects within an image, returning bounding box coordinates and labels. This directly meets the requirement to 'detect objects' in the solution.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
OCR (legacy)
Why it's wrong here
Legacy API; Read is recommended.
Facial detection
Why it's wrong here
Not required for the given requirements.
Object detection
Why this is correct
Detects objects in images.
Related concept
Read the scenario before looking for a memorised answer.
Image tagging
Why it's wrong here
Tagging assigns keywords, not object bounding boxes.
Read (OCR)
Why this is correct
Extracts printed text from images.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often confuse Image Tagging (which only provides labels) with Object Detection (which provides both labels and spatial localization), and may mistakenly choose the legacy OCR API instead of the modern Read API for text extraction.
Trap categories for this question
Keyword trap
Tagging assigns keywords, not object bounding boxes.
Detailed technical explanation
Object Detection in Azure AI Vision uses a deep neural network trained on millions of images to output a list of detected objects with their bounding box coordinates (x, y, width, height) and confidence scores. The Read (OCR) API, on the other hand, leverages a newer recognition model that handles both printed and handwritten text, supporting multiple languages and page layouts, unlike the legacy OCR API which is limited and deprecated.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Plan and manage an Azure AI solution — This question tests Plan and manage an Azure AI solution — Read the scenario before looking for a memorised answer..
The correct answer is: Object detection — Option C is correct because Azure AI Vision's Object Detection capability identifies and locates objects within an image, returning bounding box coordinates and labels. This directly meets the requirement to 'detect objects' in the solution.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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.
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