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 the purpose of Azure AI Video Indexer's transcript feature?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
To automatically convert speech in videos to searchable text with timestamps
Azure AI Video Indexer's transcript feature uses automatic speech recognition (ASR) to convert spoken audio in videos into a text transcript, which is then indexed with precise timestamps for each word or phrase. This enables users to search, navigate, and analyze video content by keyword or phrase, making the video's audio content fully searchable and accessible.
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.
✗
To translate video subtitles into multiple languages
Why it's wrong here
Translation is a separate capability — the transcript feature converts speech to text in the video's original language.
✓
To automatically convert speech in videos to searchable text with timestamps
Why this is correct
Video Indexer transcribes spoken content to text with timestamps, enabling text-based search across video content.
Related concept
Read the scenario before looking for a memorised answer.
✗
To generate written scripts for producing new videos
Why it's wrong here
Script creation is a content production task — Video Indexer transcribes existing video speech.
✗
To extract text visible in video frames (on-screen text)
Why it's wrong here
On-screen text extraction uses OCR/text detection in video — transcript extraction focuses on spoken audio.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse the transcript feature (speech-to-text) with the OCR feature (on-screen text extraction) or with translation, because all three involve 'text' but serve fundamentally different purposes in Video Indexer's pipeline.
Detailed technical explanation
How to think about this question
Under the hood, Video Indexer leverages Azure Cognitive Services' Speech-to-Text API, which uses deep neural network models trained on diverse acoustic and language datasets to handle various accents, dialects, and noisy environments. The transcript is generated with word-level timestamps, enabling frame-accurate search and integration with downstream analytics like sentiment analysis or key phrase extraction. In a real-world scenario, a media company could use this to automatically index thousands of hours of archived interviews, allowing editors to instantly locate every mention of a specific topic without manual scrubbing.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
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: To automatically convert speech in videos to searchable text with timestamps — Azure AI Video Indexer's transcript feature uses automatic speech recognition (ASR) to convert spoken audio in videos into a text transcript, which is then indexed with precise timestamps for each word or phrase. This enables users to search, navigate, and analyze video content by keyword or phrase, making the video's audio content fully searchable and accessible.
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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Question Discussion
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