The answer is Image Analysis, specifically the Describe Image or Dense Captions API. This capability is correct because it uses pre-trained deep learning models to generate natural-language descriptions that capture dominant colors, objects, and scene attributes like indoor or outdoor settings, directly matching the museum’s need for detailed, automated artwork descriptions. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your understanding of Azure Computer Vision’s pre-built image analysis features, often contrasting Dense Captions with Optical Character Recognition or object detection—common traps that only extract text or bounding boxes, not full sentences. A key memory tip: think of “Dense” as “dense details” in a single flowing caption, unlike simpler tagging or categorization.
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 automatically generate detailed descriptions of artwork for a mobile app. For each painting, the app should produce a natural-language description that includes the dominant colors, the objects present in the scene, and whether the scene is indoor or outdoor. Which Azure Computer Vision capability is best suited for this task?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Why wrong: OCR extracts printed text from images, not a natural-language description of the scene content.
B
Image Analysis (Describe Image / Dense Captions)
Image Analysis can generate descriptive captions that include objects, colors, and scene context, meeting all requirements.
C
Face API
Why wrong: Face API is specialized for detecting and analyzing human faces, not for general scene description.
D
Object Detection
Why wrong: Object Detection locates and labels objects but does not produce a natural-language description or infer scene attributes like indoor/outdoor.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Image Analysis (Describe Image / Dense Captions)
Image Analysis with the Describe Image or Dense Captions API is specifically designed to generate human-readable sentences summarizing the content of an image, including dominant colors, objects, and scene attributes like indoor/outdoor. This capability uses pre-trained deep learning models to produce natural-language descriptions, making it the ideal choice for the museum's requirement of detailed, automated artwork descriptions.
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 text from images, not a natural-language description of the scene content.
✓
Image Analysis (Describe Image / Dense Captions)
Why this is correct
Image Analysis can generate descriptive captions that include objects, colors, and scene context, meeting all requirements.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Face API
Why it's wrong here
Face API is specialized for detecting and analyzing human faces, not for general scene description.
✗
Object Detection
Why it's wrong here
Object Detection locates and labels objects but does not produce a natural-language description or infer scene attributes like indoor/outdoor.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Object Detection (which only identifies objects and their locations) with the full scene understanding and natural-language generation provided by the Describe Image / Dense Captions API, leading them to select option D.
Detailed technical explanation
How to think about this question
The Describe Image API uses a combination of convolutional neural networks (CNNs) for feature extraction and a language model (e.g., a recurrent neural network or transformer) to generate captions. Dense Captions extends this by producing multiple, region-specific descriptions, which could be leveraged to separately describe dominant colors, objects, and scene context. Under the hood, the API returns a confidence score for each caption, and the 'description.tags' array provides a list of detected concepts (e.g., 'indoor', 'painting', 'blue') that support the generated text.
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 (Describe Image / Dense Captions) — Image Analysis with the Describe Image or Dense Captions API is specifically designed to generate human-readable sentences summarizing the content of an image, including dominant colors, objects, and scene attributes like indoor/outdoor. This capability uses pre-trained deep learning models to produce natural-language descriptions, making it the ideal choice for the museum's requirement of detailed, automated artwork descriptions.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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