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AIF-C01 Practice Question: Building a generative AI application using Amazon…
A company is building a generative AI application using Amazon Bedrock. They need to implement a RAG pipeline that ingests PDF documents, processes them, and stores embeddings for retrieval. Which THREE steps are essential in this pipeline?
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
✓
Chunking the documents into smaller pieces
Document ingestion involves chunking documents into manageable pieces, generating embeddings for each chunk, and storing those embeddings in a vector store for similarity search. Prompt augmentation is part of the retrieval step, not ingestion.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fine-tuning an LLM on the ingested documents
Why it's wrong here
Fine-tuning is not part of the ingestion pipeline; RAG uses retrieval without fine-tuning.
- ✓
Chunking the documents into smaller pieces
Why this is correct
Chunking is necessary to break large documents into segments that can be embedded and retrieved accurately.
- ✓
Storing the embeddings in a vector store such as Amazon OpenSearch Serverless
Why this is correct
A vector store is required to index embeddings and support efficient similarity search.
- ✓
Generating embeddings for each chunk using an embedding model
Why this is correct
Embeddings are the vector representations needed for semantic search in the vector store.
- ✗
Augmenting the prompt with retrieved chunks at query time
Why it's wrong here
Prompt augmentation happens during inference, not during the ingestion pipeline.
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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.