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AIF-C01 Practice Question 24: Managed Assistant vs Custom Build

AWS Certified AI Practitioner (AIF-C01) · Test 3 · question 4 of 10

Fundamentals of GenAImedium

A 200-person company wants an internal assistant that answers staff questions from documents already stored in Microsoft SharePoint and Amazon S3, grounds its answers in those documents, and only surfaces content each employee is allowed to see. The IT team has no ML engineers, no front-end developers to spare, and wants something running in weeks rather than months. Which approach best meets these constraints?

Select one answer
Show answer and explanation

Answer:

  • D. Adopt Amazon Quick and use its built-in integrations to index the existing document repositories

Amazon Quick is a managed AI assistant for work. Its knowledge bases ship with built-in integrations for sources including SharePoint and Amazon S3, index that content for semantic search, keep it in sync on a schedule, and apply access control so users only receive information from knowledge bases they are authorized to access. The team gets retrieval, grounding, access control and a chat experience as configuration rather than code, which is what a group with no ML or front-end capacity and a weeks-long timeline needs (option D).

- A is wrong not because it cannot work (it can, and it is the right call when you need a custom workflow or your own UI), but here it is the more expensive path. The team would build, secure and operate the chat interface and the per-user permission filtering that the managed service already provides. - B is wrong because fine-tuning teaches style and task behaviour, not an up-to-date, attributable set of facts. A fine-tuned model cannot point at the document it answered from and has no concept of per-user document permissions. - C is wrong because training a language model from scratch is the most expensive and slowest option by a wide margin, needs specialist skills the team does not have, and still solves neither grounding nor access control.

Reference: Knowledge bases in Amazon Quick

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