- A
Generating a very long and detailed caption for the entire image
Why wrong: Long whole-image captions are still single captions — dense captioning generates many captions for different image regions simultaneously.
- B
Generating multiple region-specific captions each with a bounding box for different image areas
Dense captioning produces per-region natural language descriptions — richer than a single caption for accessibility and content analysis.
- C
Adding caption text overlaid on top of the image like movie subtitles
Why wrong: Caption overlay is video editing — dense captioning is an API feature returning structured region-caption data, not image editing.
- D
Captions that include technical details like camera settings and lighting conditions
Why wrong: Technical photo metadata is EXIF data — dense captioning describes visual content in natural language for each image region.
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.
What is 'dense captioning' in Azure AI Vision v4.0?
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
Generating multiple region-specific captions each with a bounding box for different image areas
Dense captioning in Azure AI Vision v4.0 goes beyond describing the entire image; it identifies multiple distinct regions within the image and generates a separate caption for each region, along with a bounding box that pinpoints its location. This allows for granular understanding of complex scenes, such as recognizing 'a dog on a couch' and 'a lamp on a table' as separate, localized 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.
- ✗
Generating a very long and detailed caption for the entire image
Why it's wrong here
Long whole-image captions are still single captions — dense captioning generates many captions for different image regions simultaneously.
- ✓
Generating multiple region-specific captions each with a bounding box for different image areas
Why this is correct
Dense captioning produces per-region natural language descriptions — richer than a single caption for accessibility and content analysis.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Adding caption text overlaid on top of the image like movie subtitles
Why it's wrong here
Caption overlay is video editing — dense captioning is an API feature returning structured region-caption data, not image editing.
- ✗
Captions that include technical details like camera settings and lighting conditions
Why it's wrong here
Technical photo metadata is EXIF data — dense captioning describes visual content in natural language for each image region.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse dense captioning with standard image captioning (Option A), assuming 'dense' simply means a longer or more detailed single caption, rather than recognizing it as a region-specific, multi-caption feature with bounding boxes.
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Vision v4.0 uses a neural network that combines object detection with a language model; it first identifies candidate regions via a region proposal network, then generates a caption for each region using a transformer-based decoder. A subtle behavior is that the model can produce overlapping bounding boxes for the same object from different perspectives (e.g., 'a red car' and 'a sports car'), and the API returns a confidence score for each caption-region pair. In a real-world scenario, this is invaluable for accessibility tools that need to describe every element in a photo to a visually impaired user, or for automated content moderation that must pinpoint specific areas of concern.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Describe features of computer vision workloads on Azure — study guide chapter
Learn the concepts, then practise the questions
- →
Describe features of computer vision workloads on Azure practice questions
Targeted practice on this topic area only
- →
All AI-900 questions
1,020 questions across all exam domains
- →
Microsoft Azure AI Fundamentals AI-900 study guide
Full concept coverage aligned to exam objectives
- →
AI-900 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Describe Artificial Intelligence workloads and considerations practice questions
Practise AI-900 questions linked to Describe Artificial Intelligence workloads and considerations.
Describe fundamental principles of machine learning on Azure practice questions
Practise AI-900 questions linked to Describe fundamental principles of machine learning on Azure.
Describe features of computer vision workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of computer vision workloads on Azure.
Describe features of Natural Language Processing workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of Natural Language Processing workloads on Azure.
Describe features of generative AI workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of generative AI workloads on Azure.
AI-900 fundamentals practice questions
Practise AI-900 questions linked to AI-900 fundamentals.
AI-900 scenario practice questions
Practise AI-900 questions linked to AI-900 scenario.
AI-900 troubleshooting practice questions
Practise AI-900 questions linked to AI-900 troubleshooting.
Practice this exam
Start a free AI-900 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: Generating multiple region-specific captions each with a bounding box for different image areas — Dense captioning in Azure AI Vision v4.0 goes beyond describing the entire image; it identifies multiple distinct regions within the image and generates a separate caption for each region, along with a bounding box that pinpoints its location. This allows for granular understanding of complex scenes, such as recognizing 'a dog on a couch' and 'a lamp on a table' as separate, localized 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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.