- A
Generating a text transcript summary of what was said in the video
Why wrong: Text transcript summarisation is NLP — video summarisation creates a shorter video from the best clips of a longer video.
- B
Automatically creating a highlight reel of the most informative video segments from a longer video
Video summarisation analyses content and selects the best clips — turning hours of video into a concise watchable summary.
- C
Compressing video file size while maintaining acceptable visual quality
Why wrong: Video compression is media encoding — summarisation is an AI-powered content selection process.
- D
Adding automatic chapter markers and timestamps to a video for navigation
Why wrong: Chapter markers are a specific Video Indexer feature — summarisation creates an edited shorter video, not just navigation markers.
Quick Answer
The correct answer is that video summarization in Azure Video Indexer automatically creates a highlight reel of the most informative video segments from a longer video. This works by using AI models to analyze visual content, audio cues, and scene dynamics—detecting key moments like changes in activity, faces, or objects—and then stitching those segments into a concise, condensed video output. On the AI-900 exam, this concept tests your understanding of Azure Video Indexer’s core capabilities, often appearing in questions that distinguish it from features like transcript generation or chapter markers; a common trap is confusing it with simple text-based summaries. Remember the memory tip: “Reel, not reveal”—video summarization outputs a shortened video reel, not a written transcript or index.
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 'video summarisation' in Azure Video Indexer and how does it work?
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
Automatically creating a highlight reel of the most informative video segments from a longer video
Video summarization in Azure Video Indexer automatically creates a highlight reel by selecting the most informative and visually interesting segments from a longer video. It uses AI models to analyze visual content, audio, and scene dynamics to identify key moments, such as changes in activity, faces, or objects, and then stitches these segments into a concise summary. This is distinct from transcript generation or chapter markers, as it focuses on extracting a condensed video output rather than text or navigation aids.
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 text transcript summary of what was said in the video
Why it's wrong here
Text transcript summarisation is NLP — video summarisation creates a shorter video from the best clips of a longer video.
- ✓
Automatically creating a highlight reel of the most informative video segments from a longer video
Why this is correct
Video summarisation analyses content and selects the best clips — turning hours of video into a concise watchable summary.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Compressing video file size while maintaining acceptable visual quality
Why it's wrong here
Video compression is media encoding — summarisation is an AI-powered content selection process.
- ✗
Adding automatic chapter markers and timestamps to a video for navigation
Why it's wrong here
Chapter markers are a specific Video Indexer feature — summarisation creates an edited shorter video, not just navigation markers.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'video summarization' with 'transcript summarization' (Option A), because both involve summarization, but the key distinction is that video summarization outputs a video clip, not text.
Detailed technical explanation
How to think about this question
Under the hood, Azure Video Indexer uses a combination of computer vision models (e.g., for object detection, face recognition, and motion analysis) and audio analysis (e.g., speech transcription and sentiment detection) to score each segment of the video. It then applies a ranking algorithm to select the top segments based on criteria like visual diversity, presence of key entities, and audio salience, and concatenates them into a summary video. In a real-world scenario, this is used for security camera footage review, where hours of video are condensed into minutes showing only events with human activity or unusual motion, saving analysts significant time.
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.
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: Automatically creating a highlight reel of the most informative video segments from a longer video — Video summarization in Azure Video Indexer automatically creates a highlight reel by selecting the most informative and visually interesting segments from a longer video. It uses AI models to analyze visual content, audio, and scene dynamics to identify key moments, such as changes in activity, faces, or objects, and then stitches these segments into a concise summary. This is distinct from transcript generation or chapter markers, as it focuses on extracting a condensed video output rather than text or navigation aids.
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 →
Keep practising
More AI-900 practice questions
- A company deploys an AI system to screen job applications. The system is a complex neural network that learns patterns f…
- What is 'model versioning' and why is it essential in MLOps?
- What is 'AI transparency' in Microsoft's Responsible AI principles?
- A company uses Azure OpenAI Service to generate marketing copy. They notice that sometimes the generated text contains r…
- A data scientist is training a regression model to predict house prices using features like square footage, number of be…
- A company uses Azure OpenAI Service to generate marketing copy. They want to ensure that the generated text does not con…
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.