- A
Fine-tuning an LLM on the ingested documents
Why wrong: Fine-tuning is not part of the ingestion pipeline; RAG uses retrieval without fine-tuning.
- B
Chunking the documents into smaller pieces
Chunking is necessary to break large documents into segments that can be embedded and retrieved accurately.
- C
Storing the embeddings in a vector store such as Amazon OpenSearch Serverless
A vector store is required to index embeddings and support efficient similarity search.
- D
Generating embeddings for each chunk using an embedding model
Embeddings are the vector representations needed for semantic search in the vector store.
- E
Augmenting the prompt with retrieved chunks at query time
Why wrong: Prompt augmentation happens during inference, not during the ingestion pipeline.
AIF-C01 Practice Question: Building a generative AI application using Amazon…
This AIF-C01 practice question tests your understanding of aif-c01 exam topics. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Augmenting the prompt with retrieved chunks at query time
Why it's wrong here
Prompt augmentation happens during inference, not during the ingestion pipeline.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which AIF-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AIF-C01 question wrong?
Identify which AIF-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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.
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