AI-900 Practice Question: Describe features of computer vision workloads on Azure
What is 'video action recognition' in computer vision?
⚠ Common exam trap
Candidates often confuse a specific application (like content moderation in Option C) with the general computer vision capability, leading candidates to pick a narrower, use-case-driven answer instead of the broad technical definition.
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
✓
Identifying human activities (running, cooking, falling) from temporal patterns across video frames
Video action recognition is a computer vision technique that analyzes sequences of video frames to identify and classify human activities based on temporal patterns and motion cues. Option B correctly describes this as identifying activities like running, cooking, or falling from temporal patterns across frames, which is the core definition used in Azure Video Indexer and other AI services.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Recognising which video format (MP4, MOV) an uploaded file uses
Why it's wrong here
Recognising a video's file format (MP4, MOV) is a simple metadata and container inspection task, where software reads the file's header or extension to determine its encoding structure. This requires no analysis of the visual content, temporal dynamics, or human motion. Action recognition, in contrast, operates on the decoded video frames to interpret semantic activities, making format detection fundamentally a non-vision file-parsing operation.
- ✓
Identifying human activities (running, cooking, falling) from temporal patterns across video frames
Why this is correct
Action recognition in computer vision specifically classifies what a person is doing by analysing sequences of frames, where the model learns spatiotemporal features from movement and pose changes over time. Detecting running, cooking, or falling requires modelling motion dynamics and temporal dependencies, not just static object appearances. This capability underlies sports analytics, elderly fall detection, and automated surveillance behaviour monitoring.
- ✗
Detecting when inappropriate actions are performed in user-generated video content
Why it's wrong here
Detecting inappropriate actions in user-generated content is a content moderation use case that can leverage action recognition, but it narrows the scope to a particular safety policy rather than defining the core computer vision capability. The broader technology classifies any human activity (e.g., waving, drinking, fighting) without necessarily judging whether that action should be allowed. Thus, this option confuses an application-specific policy task with the general underlying technique.
- ✗
Tracking when viewers take actions (like, share, comment) in response to a video
Why it's wrong here
Tracking viewer interactions such as likes, shares, and comments measures engagement through web analytics, event tracking, and platform API calls—it does not involve analysing visual frames of the video itself. Action recognition processes the video's pixel content to identify human motion and activities occurring inside the scene. Viewer-action tracking is behavioural telemetry about the audience, not a vision-based perceptual task.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Indexer
An indexer is a software component that processes content and builds a searchable index so that users can quickly find documents, data, or files based on keywords or phrases.
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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