hardMultiple Select
AIF-C01 Practice Question: A developer is using Amazon Bedrock to build a…
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)
⚠ Common exam trap
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.
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 long documents into smaller segments with overlap and indexing them in a vector database is the standard RAG approach for handling documents that exceed the model's context window while preserving semantic continuity across chunk boundaries. Option B is correct because setting maxTokens appropriately ensures the model has enough room to generate complete, accurate answers without truncation or exceeding the model's context window when combined with retrieved context. Option E is correct because Amazon Bedrock Knowledge Bases can use Amazon OpenSearch Serverless with the vector engine as the vector store to persist and retrieve document chunk embeddings for semantic search. Option C is not selected because simply using a larger context window model does not by itself solve retrieval accuracy for a large knowledge base and is not one of the required strategies here. Option D is not selected because lowering temperature to 0 affects randomness, not retrieval accuracy or handling of long documents, and deterministic output is not a stated requirement.
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.
- ✓
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.
- ✗
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.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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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.