A publisher states a business requirement that every one of roughly 20,000 manuscripts submitted during the day must have a summary stored before the editorial meeting the following morning. No person waits on any individual summary, and the finance owner has set a fixed monthly ceiling for the whole job. Which infrastructure requirement correctly follows from that business requirement?
- AThe summaries must be produced through realtime Messages API calls issued in a wide parallel fan-out, so that the whole set completes as early in the night as possible.
- BThe summaries must be submitted as an asynchronous batch job with a per-item identifier, since the requirement sets a deadline for the whole set rather than a response time for any one item. Correct
- CThe summaries must be generated on demand when an editor opens a manuscript, so that the system stores nothing overnight and consumes capacity only for manuscripts that are actually read.
- DThe summaries must be produced by the largest available model with a reduced output token limit, so that quality is maximised while the fixed monthly ceiling is respected.
Why A is wrong: Tempting because parallel realtime calls do finish sooner, but nothing in the requirement values early completion, and a wide synchronous fan-out spends more per manuscript and pushes against request throughput limits for no stated benefit.
Why B is correct: Correct because the stated business requirement is a bulk overnight deadline with no waiting user, which is exactly the latency-tolerant shape the Message Batches API serves, and a per-item identifier lets each summary be matched back to its manuscript.
Why C is wrong: Tempting as a cost saving, but it contradicts the stated requirement that every summary is stored before the meeting, and it converts a latency-tolerant job into one that blocks an editor at the moment of reading.
Why D is wrong: Tempting because it appears to trade quality against cost, but a reduced output limit truncates summaries rather than saving money proportionally, and model size is not what the stated deadline and ceiling actually constrain.