AIF-C01 Fundamentals of Generative AI Practice Question
Which TWO AWS services can be used to build a chatbot that responds to customer inquiries using a company's documentation as source? (Select two.)
⚠ Common exam trap
AWS often tests the distinction between services that provide conversational interfaces (like Lex) versus those that enable retrieval-augmented generation from custom data sources (like Bedrock with RAG or Q Business), leading candidates to mistakenly select Lex because it is a chatbot service, even though it lacks native document retrieval capabilities.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Amazon Bedrock with RAG
Amazon Bedrock with RAG (A) is correct because it lets you build generative AI chatbots that retrieve relevant passages from your company documentation (stored in services like Amazon S3 or OpenSearch) and pass them to a foundation model as context, so answers are grounded in the source material. Amazon Q Business (C) is correct because it is a fully managed generative AI assistant that natively connects to enterprise data sources (S3, SharePoint, Confluence, etc.), indexes the documentation, and answers customer inquiries with citations from that content. Amazon Polly (B) is only a text-to-speech service and cannot retrieve or reason over documentation, Amazon Transcribe (D) only converts speech to text, and Amazon Lex (E) builds conversational interfaces/intents but does not itself perform retrieval-augmented generation over a company's documentation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Amazon Bedrock with RAG
Why this is correct
Amazon Bedrock supplies foundation models, while Retrieval Augmented Generation grounds responses in the company's own documentation by retrieving relevant passages and passing them to the model, satisfying the requirement to answer inquiries from that source rather than relying on pretrained knowledge alone.
- ✗
Amazon Polly
Why it's wrong here
Polly converts text into speech, so it supplies voice output rather than the retrieval and generation a documentation-grounded chatbot needs. It would be correct when adding spoken responses to an existing bot. The required services are Amazon Bedrock for generation and Amazon Kendra for searching the documentation.
- ✓
Amazon Q Business
Why this is correct
Amazon Q Business connects directly to enterprise document repositories and uses retrieval-augmented generation to answer natural-language queries grounded in that indexed content, satisfying the requirement to respond from the company's own documentation. It provides the managed retrieval and generation layer a documentation-grounded chatbot needs without custom pipeline development.
- ✗
Amazon Transcribe
Why it's wrong here
Transcribe converts speech to text, handling audio input rather than answering questions from documentation. It would be correct for transcribing customer calls or voice queries. Building the chatbot requires Amazon Bedrock to generate answers and Amazon Kendra to retrieve relevant passages from the company's documents.
- ✗
Amazon Lex
Why it's wrong here
Amazon Lex builds conversational interfaces but does not retrieve answers from company documentation; that requires a knowledge-base service such as Amazon Bedrock Knowledge Bases or Kendra. Lex is tempting because it handles intent recognition and dialogue flow, and would be correct for a bot with fixed, pre-scripted responses rather than document-grounded answers.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.