- A
A quiz application that shows images and asks users multiple-choice questions
Why wrong: Image quizzes are user-facing educational apps — VQA is an AI capability for answering natural language questions about images.
- B
AI that answers natural language questions about the content of a specific image
VQA combines vision and language understanding — answering 'what colour is the car?' or 'how many people?' from image analysis.
- C
An interview format where candidates answer questions while being recorded on video
Why wrong: Video interviews are HR tools — VQA is a computer vision + NLP task for AI answering questions about image content.
- D
Generating images in response to visual prompts provided by the user
Why wrong: Image generation from prompts is DALL-E/text-to-image — VQA answers text questions about existing images.
Quick Answer
The correct answer is that visual question answering (VQA) is an AI that answers natural language questions about the content of a specific image. This is correct because VQA is a multi-modal AI capability that fuses computer vision to interpret the image with natural language processing to understand the question, then generates a relevant textual answer based on both inputs. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how Azure AI Services combine vision and language workloads, often appearing in scenarios about custom vision or the Computer Vision API. A common trap is confusing VQA with simple image captioning—captioning describes the whole scene, while VQA answers a specific query about it. For a memory tip, think of VQA as “show and tell with a question”: the AI looks at the picture, hears your question, and tells you the answer.
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. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
What is 'visual question answering' (VQA) in multi-modal AI?
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.
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.
- ✗
A quiz application that shows images and asks users multiple-choice questions
Why it's wrong here
Image quizzes are user-facing educational apps — VQA is an AI capability for answering natural language questions about images.
- ✓
AI that answers natural language questions about the content of a specific image
Why this is correct
VQA combines vision and language understanding — answering 'what colour is the car?' or 'how many people?' from image analysis.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
An interview format where candidates answer questions while being recorded on video
Why it's wrong here
Video interviews are HR tools — VQA is a computer vision + NLP task for AI answering questions about image content.
- ✗
Generating images in response to visual prompts provided by the user
Why it's wrong here
Image generation from prompts is DALL-E/text-to-image — VQA answers text questions about existing images.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that 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.
Detailed technical explanation
How to think about this question
Under the hood, VQA models typically use a deep neural network with separate encoders for the image (e.g., a CNN like ResNet or a vision transformer) and the question (e.g., an LSTM or BERT), then fuse these representations via attention mechanisms to predict an answer from a fixed vocabulary or generate a free-form text response. A real-world scenario is in accessibility tools where a visually impaired user can ask 'What color is the car in this photo?' and the AI must correctly identify the object and its attribute from the image.
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.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
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: 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.
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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Last reviewed: Jun 11, 2026
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.
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