AIF-C01 Applications of Foundation Models Practice Question
A company uses Amazon Bedrock to generate product descriptions. They notice that the output sometimes contains incorrect information. What should they do to improve accuracy?
⚠ Common exam trap
AWS often tests the misconception that simply using a larger or more powerful model (Option C) is the universal fix for accuracy issues, when in fact the root cause of hallucinations is often a lack of grounded, retrievable context that RAG specifically addresses.
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
✓
Implement Retrieval-Augmented Generation (RAG).
Retrieval-Augmented Generation (RAG) enhances the accuracy of foundation model outputs by grounding the generation in authoritative, up-to-date external knowledge sources. Instead of relying solely on the model's parametric memory, RAG retrieves relevant documents or data from a vector database (e.g., Amazon OpenSearch Serverless) and injects them into the prompt context, reducing hallucinations and incorrect information in product descriptions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the temperature parameter.
Why it's wrong here
Raising temperature increases sampling randomness, producing more varied and creative text, which worsens factual drift rather than improving accuracy. Higher temperature suits brainstorming or marketing copy where diversity is wanted; accuracy tasks need it lowered, ideally near zero.
- ✓
Implement Retrieval-Augmented Generation (RAG).
Why this is correct
Retrieval-Augmented Generation grounds responses in authoritative source documents retrieved at inference time, so the model cites real product data instead of relying solely on parametric memory. This directly reduces fabricated or incorrect details in generated descriptions.
- ✗
Use a larger foundation model.
Why it's wrong here
A larger foundation model does not guarantee factual correctness; scale improves capability, not grounding, so hallucinations can persist. Larger models suit complex reasoning or nuanced generation, but the accuracy fix here is retrieval augmentation or guardrails tying output to verified product data.
- ✗
Use AWS WAF to filter outputs.
Why it's wrong here
AWS WAF filters inbound HTTP traffic against web exploits; it cannot inspect or correct model-generated text, so factual errors pass through untouched. WAF would be right for blocking SQL injection or bot traffic at the edge, not for grounding Bedrock outputs in accurate source data.
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.