AI & Technical question

Provide an overview of a media app architecture.

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Technical breadth for a media product's end to end architecture, from ingestion to playback.

Start with content ingestion: raw video upload or live encoder feed entering the pipeline.
Describe transcoding: converting source video into multiple bitrate and resolution renditions for adaptive streaming.
Describe packaging and storage: segmenting video into chunks and storing them alongside a manifest file in object storage.
Describe delivery: a CDN caching and serving segments close to users to reduce latency and load.
Describe the client player: requesting the manifest, selecting renditions adaptively based on network conditions.
Mention supporting systems: DRM for protected content, analytics for playback quality monitoring, and recommendation services.

Walks through the pipeline in the correct order, ingestion, transcoding, packaging, delivery, playback, rather than a random list of components.
Names concrete technologies at each stage, like a CDN for delivery and adaptive bitrate manifests for playback.
Includes DRM and playback quality analytics as supporting systems, not just the core streaming path.
Distinguishes live ingestion from on-demand upload, since architecture differs meaningfully between them.

Listing components with no sense of the pipeline order or data flow.
Ignoring the CDN's role, which is central to how media reaches users reliably at scale.

How would this architecture change for a live sports broadcast versus on-demand video?
How would you monitor playback quality across millions of sessions?