The answer is Image Analysis. This is the correct choice because Azure’s Image Analysis service offers pre-built, ready-to-use capabilities for both generating descriptive captions—via its ‘describe’ operation—and classifying images into categories like portrait or landscape, all without requiring any custom model training. On the AI-900 exam, this question tests your understanding of when to use pre-built Computer Vision services versus Custom Vision; a common trap is assuming you need to train a model for captioning or basic classification, but Image Analysis handles both out of the box. Remember that Custom Vision is only needed when you must train on your own labeled data for specialized or niche categories. For the museum’s dual need of caption generation and orientation classification, Image Analysis is the single, no-training-required solution. Memory tip: think “Image Analysis = instant captions + built-in categories,” while “Custom Vision = your own custom labels.”
AI-900 Practice Question: Describe features of computer vision workloads on Azure
This AI-900 practice question tests your understanding of describe features of computer vision workloads on azure. 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.
A museum wants to create an interactive exhibit where visitors can take a photo of a painting. The system should then generate a descriptive caption (e.g., 'A woman with a pearl earring') and classify the painting as either a portrait or landscape. Which Azure Computer Vision capability should they use without needing to train a custom model?
Why wrong: Custom Vision requires training a custom model, but the scenario explicitly wants a prebuilt capability without custom training.
B
Image Analysis
Azure Image Analysis prebuilt model can describe image content in natural language and categorize images into various categories, including portrait and landscape.
C
Face Detection
Why wrong: Face Detection analyzes human faces, not artworks or their styles.
D
Optical Character Recognition (OCR)
Why wrong: OCR extracts text from images, but the scenario requires description and classification of the image content, not text extraction.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Image Analysis
Image Analysis in Azure Computer Vision provides pre-built capabilities for extracting rich information from images, including generating human-readable captions (via the 'describe' operation) and classifying images into categories like 'portrait' or 'landscape' without requiring any custom training. This directly matches the museum's need for both caption generation and orientation classification using a pre-trained model.
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.
✗
Custom Vision
Why it's wrong here
Custom Vision requires training a custom model, but the scenario explicitly wants a prebuilt capability without custom training.
✓
Image Analysis
Why this is correct
Azure Image Analysis prebuilt model can describe image content in natural language and categorize images into various categories, including portrait and landscape.
Related concept
Read the scenario before looking for a memorised answer.
✗
Face Detection
Why it's wrong here
Face Detection analyzes human faces, not artworks or their styles.
✗
Optical Character Recognition (OCR)
Why it's wrong here
OCR extracts text from images, but the scenario requires description and classification of the image content, not text extraction.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Custom Vision (which requires training) with Image Analysis (which is pre-built), or mistakenly think Face Detection or OCR can generate descriptive captions, when in fact they are specialized for different tasks.
Trap categories for this question
Scenario analysis trap
Custom Vision requires training a custom model, but the scenario explicitly wants a prebuilt capability without custom training.
Detailed technical explanation
How to think about this question
Under the hood, Image Analysis uses deep neural networks trained on millions of images to perform tasks like object detection, scene classification, and caption generation via the 'describe' API, which returns a list of captions with confidence scores. The classification into 'portrait' or 'landscape' is part of the 'analyze' operation's category taxonomy, which includes over 80 categories such as 'portrait' and 'landscape_art'. This pre-trained model can handle diverse artistic styles without fine-tuning, making it ideal for the museum's interactive exhibit.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Describe features of computer vision workloads on Azure — This question tests Describe features of computer vision workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Image Analysis — Image Analysis in Azure Computer Vision provides pre-built capabilities for extracting rich information from images, including generating human-readable captions (via the 'describe' operation) and classifying images into categories like 'portrait' or 'landscape' without requiring any custom training. This directly matches the museum's need for both caption generation and orientation classification using a pre-trained model.
What should I do if I get this AI-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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
This AI-900 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-900 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.