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?
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
The issue likely stems from the chunking and retrieval strategy. If the retrieved document chunks do not contain the exact answer or are poorly segmented, the model may not have the necessary context. Improving chunking to be more semantic and ensuring retrieval uses a relevant similarity metric (e.g., using Kendra's relevance tuning) would help. Increasing temperature or reducing tokens would degrade quality. Switching model may not address 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 may improve quality but does not fix the underlying retrieval issues; it's a band-aid and more expensive.
- ✗
The model's temperature is set too high; reduce it to 0.1
Why it's wrong here
High temperature increases randomness but the symptoms point to inaccurate context, not randomness.
- ✗
The maximum tokens limit is too low; increase it to 4096
Why it's wrong here
While a low limit could cut off context, the logs indicate retrieved documents are passed; length is not the primary issue.
- ✓
The chunking strategy for documents is too coarse or inappropriate; refine chunking and use semantic search in Kendra
Why this is correct
Proper chunking ensures each chunk contains coherent information relevant to potential queries; Kendra's semantic search improves relevance.
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 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.