AIF-C01 Fundamentals of Generative AI Practice Question
Exhibit
Refer to the exhibit.
Exhibit:
```json
"KnowledgeBaseConfiguration": {
"type": "SEMANTIC",
"vectorKnowledgeBaseConfiguration": {
"embeddingModelArn": "arn:aws:bedrock:us-east-1::foundation-model/amazon.titan-embed-text-v1"
}
}
```An application uses this configuration to enable RAG. What is required for the knowledge base to function?
⚠ Common exam trap
Watch out — candidates often think the embedding model must be fine-tuned or that internet access is needed, but the core requirement is the vector index in a vector store like Amazon OpenSearch Serverless on AWS, which is essential for the retrieval step in RAG.
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
✓
The knowledge base must have a vector index configured in Amazon OpenSearch Serverless
For a knowledge base to function in a RAG (Retrieval-Augmented Generation) setup on AWS, the knowledge base must have a vector index configured in Amazon OpenSearch Serverless. This vector index stores the embeddings generated from the source documents, enabling efficient similarity search to retrieve relevant context for the agent. Without a vector index, the knowledge base cannot perform the vector search required to fetch relevant document chunks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The agent must have internet access to retrieve documents
Why it's wrong here
Retrieval happens against the configured knowledge base store, not the public internet, so outbound web access is irrelevant here. It is tempting because open-web retrieval agents do need internet connectivity, and that would be the correct requirement for a browsing or web-search tool rather than a managed knowledge base.
- ✗
The embedding model ARN must include the account ID
Why it's wrong here
The embedding model ARN identifies the resource for permissions; the knowledge base functions without the account ID being embedded in that string. It is tempting because ARNs commonly include the account ID, and that format would be correct when the resource policy or cross-account access requires the full qualified ARN.
- ✗
The embedding model must be fine-tuned on the domain data
Why it's wrong here
RAG supplies domain context at inference time through retrieved passages, so the embedding model needs no fine-tuning to make the knowledge base work. Fine-tuning is tempting because it genuinely adapts a model to domain vocabulary, and it would be the right choice when task-specific behaviour must be baked into model weights.
- ✓
The knowledge base must have a vector index configured in Amazon OpenSearch Serverless
Why this is correct
RAG retrieval depends on semantic similarity search, which requires vector embeddings stored in an index. Amazon OpenSearch Serverless provides that vector index, so the knowledge base can match queries to relevant document chunks before passing them to the model.
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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JA
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.