The answer is Azure Computer Vision, the correct Azure AI service for image analysis that can detect people, objects, brands, and text within images. This service is designed to extract rich, pre-built visual information from images without requiring custom machine learning training, using features like object detection, brand recognition, and optical character recognition (OCR) to read embedded text. On the AZ-900 exam, this question tests your understanding of which Azure AI service maps to specific, general-purpose image analysis tasks, often appearing as a scenario-based multiple-choice question where you must distinguish Computer Vision from more specialized services like Custom Vision or Face API. A common trap is confusing Computer Vision with Custom Vision—remember that Computer Vision is for pre-built, out-of-the-box analysis, while Custom Vision requires you to train your own model. Memory tip: think of "Computer Vision" as the all-in-one "Swiss Army knife" for images, handling people, objects, brands, and text right out of the box.
AZ-900 Describe Azure architecture and services Practice Question
This AZ-900 practice question tests your understanding of describe azure architecture and services. 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.
Which Azure AI service can analyze images and return information about people, objects, brands, and text within those images?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure Computer Vision
Azure Computer Vision is the correct service because it is specifically designed to extract rich information from images, including the detection of people, objects, brands, and embedded text (via OCR). It provides a comprehensive set of pre-built image analysis capabilities without requiring custom training, making it the appropriate choice for this general-purpose scenario.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Azure Face API
Why it's wrong here
Face API specifically detects and analyzes human faces; Computer Vision handles broader image analysis.
✓
Azure Computer Vision
Why this is correct
Computer Vision analyzes images for objects, brands, text, faces, colors, and generates descriptions.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Custom Vision
Why it's wrong here
Custom Vision trains models for specific image classification; Computer Vision uses pre-built models.
✗
Azure Form Recognizer
Why it's wrong here
Form Recognizer extracts structured data from forms and documents; Computer Vision handles general image analysis.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Azure Computer Vision with Azure Custom Vision, mistakenly thinking that any image analysis requires custom training, when in fact Computer Vision provides pre-built analysis for common objects, brands, and text without any training.
Detailed technical explanation
How to think about this question
Under the hood, Azure Computer Vision uses deep neural networks trained on vast datasets to perform tasks like object detection (via bounding boxes and tags), optical character recognition (OCR) for printed and handwritten text, and brand detection by matching logos against a curated knowledge base. A subtle behavior is that the Analyze Image API can return confidence scores for detected objects and brands, and it supports domain-specific models (e.g., for celebrities or landmarks) that can be invoked via the `details` parameter. In a real-world scenario, a retail company could use Computer Vision to automatically catalog products from shelf images, identifying brand logos, product types, and any text on packaging.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AZ-900 question in full detail.
Describe Azure architecture and services — This question tests Describe Azure architecture and services — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Azure Computer Vision — Azure Computer Vision is the correct service because it is specifically designed to extract rich information from images, including the detection of people, objects, brands, and embedded text (via OCR). It provides a comprehensive set of pre-built image analysis capabilities without requiring custom training, making it the appropriate choice for this general-purpose scenario.
What should I do if I get this AZ-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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