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) (A) is correct because it grounds the model's responses in an external knowledge base retrieved from sources like Amazon OpenSearch Serverless or Aurora, injecting relevant, up-to-date context into the prompt so answers are factually accurate rather than hallucinated. Model fine-tuning (C) is correct because adapting a foundation model on domain-specific labeled data adjusts its weights to better match the company's terminology, style, and task patterns, directly raising answer quality for that use case. Prompt engineering (E) is correct because carefully designing instructions, few-shot examples, and output formatting in the prompt steers the model toward more precise, relevant, and consistent answers without retraining. Auto-scaling of provisioned throughput (B) only affects performance and cost under load, not answer correctness, and encryption at rest (D) is a security control protecting stored data, neither of which improves the accuracy of generated answers.
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
Retrieval Augmented Generation grounds responses in your own data by retrieving relevant passages and injecting them into the prompt, so answers reflect actual source content rather than parametric guesses. This directly satisfies the accuracy requirement by reducing hallucination and stale knowledge, which a question-answering system over proprietary documents demands.
- ✗
Auto-scaling of provisioned throughput
Why it's wrong here
Auto-scaling adjusts provisioned throughput capacity to match traffic, so it changes cost and latency, not the model's answer correctness. It is genuinely useful for handling variable request volumes without throttling, but accuracy in Bedrock comes from retrieval, grounding and model choice, not capacity scaling.
- ✓
Model fine-tuning
Why this is correct
Fine-tuning adjusts a foundation model's weights on domain-specific labelled data, embedding your terminology and answer patterns directly into the model rather than supplying them at inference time. This satisfies the accuracy constraint by reducing reliance on prompt context alone, yielding more consistent, domain-aligned responses for the question-answering workload.
- ✗
Encryption at rest
Why it's wrong here
Encryption at rest protects stored data from unauthorised access; it does not change retrieval or generation, so answer accuracy is unaffected. It is tempting because it is a Bedrock security feature, and would be correct when meeting compliance requirements for data stored in knowledge bases or custom models.
- ✓
Prompt engineering
Why this is correct
Prompt engineering directly shapes the model's input, raising answer accuracy by constraining the response format, supplying worked examples, and instructing the model to reason step by step before answering. It satisfies the stem's accuracy requirement without retraining, since Bedrock exposes inference parameters and system prompts for tuning behaviour at request time.
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.