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 application that automatically generates descriptive captions for uploaded photos of artworks. The captions should describe the main subject, scene, and artistic style. Which Azure Computer Vision capability should they use?
Why wrong: OCR extracts printed or handwritten text from images, but it does not generate descriptive captions about the image content.
B
Image Analysis (with description feature)
Image Analysis includes a description feature that generates human-readable captions summarizing the image content, which fits the requirement for artwork captions.
C
Face API
Why wrong: The Face API detects and analyzes human faces, such as age, emotion, and identity, but it does not describe general image content.
D
Custom Vision (object detection)
Why wrong: Custom Vision allows training custom object detection models, but it requires labeled data and is intended for recognizing specific objects, not generating descriptive captions.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Image Analysis (with description feature)
Option B is correct because the Image Analysis capability in Azure Computer Vision includes a 'description' feature that generates human-readable captions summarizing the main subject, scene, and artistic style of an image. This is achieved through pre-trained deep learning models that analyze visual content and produce natural language descriptions, making it ideal for automatically captioning artwork photos.
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.
✗
Optical Character Recognition (OCR)
Why it's wrong here
OCR extracts printed or handwritten text from images, but it does not generate descriptive captions about the image content.
✓
Image Analysis (with description feature)
Why this is correct
Image Analysis includes a description feature that generates human-readable captions summarizing the image content, which fits the requirement for artwork captions.
Related concept
Read the scenario before looking for a memorised answer.
✗
Face API
Why it's wrong here
The Face API detects and analyzes human faces, such as age, emotion, and identity, but it does not describe general image content.
✗
Custom Vision (object detection)
Why it's wrong here
Custom Vision allows training custom object detection models, but it requires labeled data and is intended for recognizing specific objects, not generating descriptive captions.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse OCR (Option A) with image description, assuming text extraction can generate captions, or they mistakenly think Custom Vision (Option D) is required for any custom analysis, when in fact the pre-built Image Analysis description feature handles general scene and style captioning without training.
Detailed technical explanation
How to think about this question
Under the hood, Azure Computer Vision's description feature uses a combination of convolutional neural networks (CNNs) for feature extraction and recurrent neural networks (RNNs) or transformer-based language models to generate captions. The service returns multiple confidence-scored captions, and the 'tags' feature can further identify specific elements like 'painting', 'landscape', or 'impressionist' to enrich the description. In a real-world scenario, a museum could use this to automatically generate alt-text for accessibility or to populate metadata for digital archives, though the captions may sometimes miss nuanced stylistic details without fine-tuning.
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 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 (with description feature) — Option B is correct because the Image Analysis capability in Azure Computer Vision includes a 'description' feature that generates human-readable captions summarizing the main subject, scene, and artistic style of an image. This is achieved through pre-trained deep learning models that analyze visual content and produce natural language descriptions, making it ideal for automatically captioning artwork photos.
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.
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Question Discussion
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