A city traffic department wants to detect when pedestrians cross a crosswalk and count them. They have a camera mounted above the crosswalk. Using Azure Vision in Foundry Tools, which operation should they configure?

Custom Vision with a pedestrian detector

Spatial analysis with a person-crossing-line operation on the crosswalk boundary

Azure AI Video Indexer with face detection

Video Indexer with object detection

Which of the following is a capability of Azure AI Video Indexer?

Translating video content in real time

Detecting and identifying people in video

Compressing video files for efficient storage

Generating 3D models from video

A retail analytics company wants to analyze customer movement in a store to optimize product placement. They have a mix of cameras: some are old with low resolution, and some are new with high resolution. They need to detect people and track their movement across the store. They also need to minimize false positives. What should they do?

Use Custom Vision to train a model on the high-resolution cameras only

Use Azure AI Language to analyze the video feeds

Use Azure AI Video Indexer to analyze all camera feeds offline

Use Azure Vision in Foundry Tools spatial analysis with a people-counting operation and adjust the detection threshold per camera

A warehouse operator wants to analyze video from forklift cameras to detect if a person enters a hazardous zone. They need to trigger an alert when a person is detected in the zone. Which Azure Vision in Foundry Tools operation should they configure?

Use the 'people counting' operation to count the number of people in the frame.

Use the 'person distance' operation to measure the distance between people.

Use the 'person in zone' operation to detect people within a defined polygon.

Use the 'person crossing line' operation to detect when a person enters the zone.

A stadium wants to analyze security camera feeds to detect crowd density and alert security if an area becomes too crowded. They need to process the video in near real-time. What should they configure?

Deploy Azure Vision in Foundry Tools with the 'people counting' operation on an edge device.

Set up a custom Azure Machine Learning model to count people in each frame.

Upload video clips to Azure AI Video Indexer for analysis every minute.

Use Azure Computer Vision's detect people API on a live stream.

Microsoft Certified:Azure AI Engineer Associate

Domain 4Objective 3

Analyze Videos AI-102 Practice Questions (Page 6)

Part of the Implement computer vision solutions domain, which accounts for 10-15% of the AI-102 exam. Microsoft does not publish an official question count, but from its 100-minute exam (~40–65 total, ~4–10 in this domain), expect 1–3 from this objective — we provide 38 practice questions to prepare you well beyond it. (estimate)

38questions here

8free pages

7concepts

10-15%of the exam

Questions 26–30

26application · medium
A city traffic department wants to detect when pedestrians cross a crosswalk and count them. They have a camera mounted above the crosswalk. Using Azure Vision in Foundry Tools, which operation should they configure?
Select an answer first
27foundation · easy
Which of the following is a capability of Azure AI Video Indexer?
Select an answer first
28expert · hard
A retail analytics company wants to analyze customer movement in a store to optimize product placement. They have a mix of cameras: some are old with low resolution, and some are new with high resolution. They need to detect people and track their movement across the store. They also need to minimize false positives. What should they do?
Select an answer first
29application · medium
A warehouse operator wants to analyze video from forklift cameras to detect if a person enters a hazardous zone. They need to trigger an alert when a person is detected in the zone. Which Azure Vision in Foundry Tools operation should they configure?
Select an answer first
30application · medium
A stadium wants to analyze security camera feeds to detect crowd density and alert security if an area becomes too crowded. They need to process the video in near real-time. What should they configure?
Select an answer first

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