AIF-C01 Fundamentals of Generative AI Practice Question
A company deployed a chatbot using Amazon Lex integrated with a Lambda function that invokes Claude on Amazon Bedrock. The Lambda function retrieves relevant documents from an Amazon Kendra index to use as context. Users report that the chatbot's responses are often irrelevant or incorrect despite the Kendra index containing accurate information. The logs show that the Lambda function is correctly passing retrieved documents to the model. What is the most likely cause and solution?
⚠ Common exam trap
AIF-C01 often tests the misconception that a bigger or more expensive model fixes RAG quality — candidates must recognize that retrieval (chunking, indexing, semantic search) is the usual bottleneck when context is already being passed.
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 chunking strategy for documents is too coarse or inappropriate; refine chunking and use semantic search in Kendra
When retrieved documents are correctly passed to the model but responses are still irrelevant, the problem is almost always upstream in retrieval quality — specifically how documents were chunked and indexed in Kendra. Coarse or poorly aligned chunks dilute the semantic signal, so the model receives context that does not actually answer the query. Refining chunking strategy and enabling semantic search in Kendra directly addresses the root cause.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to a larger foundation model like Claude 3 Opus
Why it's wrong here
A larger model cannot repair context that is retrieved but mis-ranked or truncated before the prompt; the logs only prove documents were passed, not that the relevant passages survived chunking. Larger models suit open-ended reasoning tasks, not fixing retrieval precision in a RAG pipeline.
- ✗
The model's temperature is set too high; reduce it to 0.1
Why it's wrong here
Temperature governs sampling randomness, so it produces varied phrasing, not factually wrong answers grounded in supplied context; the retrieved documents are already reaching the model. Low temperature is chosen for deterministic classification or extraction tasks, where identical inputs must yield identical outputs.
- ✗
The maximum tokens limit is too low; increase it to 4096
Why it's wrong here
A low output token cap truncates answers mid-sentence rather than making them contradict the context; the symptom is incomplete responses, not irrelevance. Raising the limit suits long-form summarisation or generation where the model must emit extended text.
- ✓
The chunking strategy for documents is too coarse or inappropriate; refine chunking and use semantic search in Kendra
Why this is correct
Coarse chunking embeds large blocks, so Kendra returns passages whose vectors dilute the specific answer, and the model receives loosely relevant context despite accurate documents. Refining chunk size and enabling semantic search sharpens retrieval precision, satisfying the requirement that passed context actually match the user's question.
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 and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.