AI-900 Practice Question: Describe features of computer vision workloads on Azure
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?
⚠ Common exam trap
It's easy for candidates to 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.
Answer choices
Why each option matters
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.
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 (Optical Character Recognition) in Azure AI Vision extracts printed or handwritten text from images and converts it into machine-readable character strings, along with bounding-box coordinates. It does not analyze or describe visual content beyond the text itself, so it cannot capture objects, spatial relationships, or scene context such as "a gallery with paintings on white walls." Therefore, it is entirely unsuitable for generating a natural-language description of museum artwork or exhibitions.
- ✓
Image Analysis (Describe Image / Dense Captions)
Why this is correct
The Image Analysis API, specifically its 'Describe Image' and 'Dense Captions' capabilities, uses vision-language models to generate human-readable sentences that summarize the entire image, including objects, colors, actions, and scene attributes such as indoor/outdoor setting. Dense Captions goes further by producing multiple captions for specific regions of the image, enabling richer, more detailed storytelling about each artifact or exhibit. This output directly satisfies the museum's requirement for automatic narration and is the correct choice.
- ✗
Face API
Why it's wrong here
The Azure AI Face API is a specialized endpoint for detecting human faces and analyzing facial attributes such as age, emotion, head pose, and glasses, as well as performing face recognition and verification. It has no capability to interpret non-face content, architectural details, paintings, sculptures, or the overall gallery layout, and it would ignore all other elements of a museum scene. Consequently, it cannot generate a coherent description of the entire image.
- ✗
Object Detection
Why it's wrong here
Object detection services, such as Azure's Detect Objects in Image, return a structured list of labeled objects with bounding-box coordinates and confidence scores, but they do not synthesize that data into a narrative or infer high-level context like "dimly lit hall with classical sculptures." The output is JSON metadata, not a fluent description, and it misses relationships between objects and scene semantics. This is why object detection fails the requirement for detailed descriptive text.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Computer vision
Computer vision is a field of artificial intelligence that enables computers to interpret and make decisions based on visual data from the world, such as images and videos.
Key term
Deep learning
Deep learning is a subset of machine learning that uses multi-layered neural networks to automatically learn patterns from large amounts of data.
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