AI-103 Practice Question 8: Video Analyzer Segment Metadata

Microsoft Azure AI Apps and Agents Developer Associate (AI-103) · Test 1 · question 8 of 10

Implement computer vision solutionsmedium

A broadcaster is indexing a 20,000-hour advertising archive so that an internal agent can answer questions like "show me the clips where our client's logo appears next to a competitor's". For every scene the team needs a natural-language description, a timestamped transcript, and two custom values, the brand on screen and the ad category, returned as structured output that can drop straight into a vector store.

Which approach fits with the least custom glue code?

Show answer and explanation

Answer:

D. Run the Content Understanding video analyzer with a custom fieldSchema and segmentation enabled

Content Understanding processes video in two stages. Content extraction produces the metadata backbone: WEBVTT transcription with diarization, key frames, and shot detection. Field extraction then uses a generative model to fill a `fieldSchema` you define (here, brand and an ad-category classification) and to segment the video. The prebuilt video analyzer emits RAG-ready Markdown and JSON with per-segment descriptions, so the output can drop straight into a vector store with no post-processing.

- A is wrong because per-frame captions describe single frames in isolation. Content Understanding's advantage is segment-based multi-frame analysis, which is what identifies actions, events, and themes; stitching independent captions also leaves you writing all the glue code the question is trying to avoid. - B is wrong because only spoken words are captured. Music, sound effects, and everything visual (including a logo that is never mentioned aloud) are missing, and a logo query is inherently visual. - C is wrong because object labels are not scene descriptions or business fields. You would get bounding-box style labels with no transcript, no segmentation, and no brand-versus-category vocabulary.

Reference: Azure Content Understanding in Foundry Tools video overview

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