AI-900 Practice Question: Describe features of computer vision workloads on Azure
What is 'visual question answering' (VQA) in multi-modal AI?
⚠ Common exam trap
A common mix-up: candidates confuse 'visual question answering' with 'image captioning' or 'image generation,' but VQA specifically requires answering a natural language question about an image, not describing it generically or creating new images.
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
✓
AI that answers natural language questions about the content of a specific image
Visual Question Answering (VQA) is a multi-modal AI capability that combines computer vision and natural language processing. The system takes an image as input along with a natural language question about that image, and outputs a relevant answer. This is correct because VQA specifically requires the AI to understand both visual content and textual queries to generate a response, which is exactly what option B describes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A quiz application that shows images and asks users multiple-choice questions
Why it's wrong here
This describes an educational or entertainment application with a predefined set of questions and answer choices. VQA, in contrast, is an open-ended AI task that accepts arbitrary natural language questions about an image and synthesizes a free-form answer using learned vision–language alignment, not a fixed quiz schema or multiple-choice selection.
- ✓
AI that answers natural language questions about the content of a specific image
Why this is correct
VQA (Visual Question Answering) is a multimodal AI task that combines computer vision to extract objects, attributes, and spatial relationships from an image with natural language processing to interpret the question and generate a coherent answer, such as identifying a color, counting objects, or reasoning about a scene.
- ✗
An interview format where candidates answer questions while being recorded on video
Why it's wrong here
This describes a structured interview or assessment tool used in human resources, not a computer vision capability. Visual Question Answering (VQA) is an AI task where a model fuses image features from a vision encoder with the semantic representation of a natural language question to produce a textual answer, not a recording or candidate evaluation process.
- ✗
Generating images in response to visual prompts provided by the user
Why it's wrong here
This option reverses the direction of the interaction. VQA takes an existing image as input plus a text question about it and outputs an answer; generating images from visual or text prompts is a generative task (e.g., DALL-E, Stable Diffusion) that creates new visual content, whereas VQA performs discriminative reasoning over a provided image.
Go deeper
Related to this question
Learn chapter
What is Computer Vision?
Key term
Question answering
Question answering is an Azure AI service that lets you build a natural language query system that extracts answers from a custom knowledge base of documents or FAQs.
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.
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