- A
Chunk documents into smaller segments with overlap, and index them in a vector database
Chunking ensures each piece fits within the context window, and overlap preserves context across chunks.
- B
Set the maxTokens parameter to a value that allows complete answers without exceeding the context window
Properly setting maxTokens ensures the model can generate full answers without being cut off, while staying within limits.
- C
Use a model with a larger context window, such as Anthropic Claude 2.1 (200K tokens)
Why wrong: While a larger context window helps, it alone does not solve the problem; chunking and retrieval are still needed for large knowledge bases.
- D
Lower the temperature to 0 to ensure deterministic responses
Why wrong: Lowering temperature reduces creativity but does not address document length or retrieval accuracy.
- E
Use a vector database like Amazon OpenSearch Serverless with vector engine to store and retrieve document chunks
A vector database enables semantic similarity search to retrieve relevant chunks for each query.
AIF-C01 Generative AI and Foundation Models Practice Question
This AIF-C01 practice question tests your understanding of generative ai and foundation models. 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 developer is using Amazon Bedrock to build a chatbot that answers questions about a large internal knowledge base. The knowledge base contains documents with varying lengths, some exceeding 10,000 tokens. The chatbot must provide accurate answers and handle queries about multiple topics. Which THREE strategies should the developer implement? (Select THREE)
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
Chunk documents into smaller segments with overlap, and index them in a vector database
Option A is correct because chunking documents into smaller segments with overlap ensures that the retrieval system can capture relevant context even when a query spans chunk boundaries, and indexing these chunks in a vector database enables efficient semantic similarity search. This approach is essential for handling documents exceeding 10,000 tokens, as it allows the model to retrieve only the most relevant pieces of information without exceeding the context window.
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.
- ✓
Chunk documents into smaller segments with overlap, and index them in a vector database
Why this is correct
Chunking ensures each piece fits within the context window, and overlap preserves context across chunks.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Set the maxTokens parameter to a value that allows complete answers without exceeding the context window
Why this is correct
Properly setting maxTokens ensures the model can generate full answers without being cut off, while staying within limits.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a model with a larger context window, such as Anthropic Claude 2.1 (200K tokens)
Why it's wrong here
While a larger context window helps, it alone does not solve the problem; chunking and retrieval are still needed for large knowledge bases.
- ✗
Lower the temperature to 0 to ensure deterministic responses
Why it's wrong here
Lowering temperature reduces creativity but does not address document length or retrieval accuracy.
- ✓
Use a vector database like Amazon OpenSearch Serverless with vector engine to store and retrieve document chunks
Why this is correct
A vector database enables semantic similarity search to retrieve relevant chunks for each query.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that a larger context window alone can handle large knowledge bases without retrieval augmentation, but the key is that retrieval-augmented generation (RAG) with chunking and vector search is required for scalable and accurate answers.
Detailed technical explanation
How to think about this question
Chunking with overlap (e.g., 200-token chunks with 50-token overlap) ensures that semantically related content is not split across chunks, improving retrieval recall. Vector databases like Amazon OpenSearch Serverless with vector engine use approximate nearest neighbor (ANN) algorithms (e.g., HNSW) to index embeddings, enabling sub-second retrieval even with millions of chunks. The maxTokens parameter in Bedrock controls the output length, but it must be set carefully to avoid truncating answers while staying within the model's context window (e.g., 4,096 tokens for Claude Instant).
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.
TExam Day Tips
- 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
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
Generative AI and Foundation Models — This question tests Generative AI and Foundation Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Chunk documents into smaller segments with overlap, and index them in a vector database — Option A is correct because chunking documents into smaller segments with overlap ensures that the retrieval system can capture relevant context even when a query spans chunk boundaries, and indexing these chunks in a vector database enables efficient semantic similarity search. This approach is essential for handling documents exceeding 10,000 tokens, as it allows the model to retrieve only the most relevant pieces of information without exceeding the context window.
What should I do if I get this AIF-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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
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