AIF-C01 Applications of Foundation Models Practice Question
A company uses Amazon Bedrock to build a question-answering system. Which THREE features of Amazon Bedrock can improve answer accuracy? (Choose three.)
⚠ Common exam trap
AWS often tests the distinction between features that improve accuracy (RAG, fine-tuning, prompt engineering) versus features that improve operational aspects like scalability (auto-scaling) or security (encryption), leading candidates to mistakenly select non-accuracy-related options.
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
✓
Retrieval Augmented Generation (RAG)
Retrieval Augmented Generation (RAG) improves answer accuracy by retrieving relevant, up-to-date information from external knowledge bases (e.g., Amazon OpenSearch Serverless or Aurora) and providing it as context to the foundation model. This grounds the model's response in factual data, reducing hallucinations and enabling accurate answers without retraining.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Retrieval Augmented Generation (RAG)
Why this is correct
RAG retrieves factual information from a knowledge base to improve answer accuracy.
- ✗
Auto-scaling of provisioned throughput
Why it's wrong here
Auto-scaling ensures performance under load but does not affect answer accuracy.
- ✓
Model fine-tuning
Why this is correct
Fine-tuning adapts the model to domain-specific language and knowledge.
- ✗
Encryption at rest
Why it's wrong here
Encryption protects data at rest but does not improve answer accuracy.
- ✓
Prompt engineering
Why this is correct
Well-structured prompts can guide the model to produce more accurate answers.
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 |
Go deeper
Related to this question
About these practice questions
This AIF-C01 question is part of Courseiva's 619-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.