AIF-C01 Fundamentals of Generative AI Practice Test 8 — 15 Questions

Question 1

A company wants to build a customer support chatbot that answers questions based on a large internal knowledge base. Which AWS service is most suitable for implementing RAG to retrieve relevant documents?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Amazon Kendra. Amazon Kendra is an intelligent enterprise search service powered by machine learning that is specifically designed for retrieving relevant documents from large knowledge bases, making it ideal for RAG implementations. It supports natural language queries and returns precise answers with citations, which is exactly what a customer support chatbot needs to retrieve relevant documents.

Answer

Amazon Lex

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Amazon Polly

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Amazon Connect

Question 2

Which THREE are best practices for building a secure and scalable generative AI application using Amazon Bedrock? (Choose 3)

Accepted Answer

Implement guardrails to filter harmful content. Option A is correct because Amazon Bedrock Guardrails let you define denied topics, content filters, word filters, and PII redaction policies that are applied to both prompts and model responses, which is a core best practice for filtering harmful or non-compliant content in a generative AI application. Option D is correct because AWS KMS provides customer-managed keys and envelope encryption for data at rest, including model artifacts, knowledge bases, and logs, satisfying security and compliance requirements for protecting sensitive data. Option E is correct because Bedrock offers a unified, serverless API to access foundation models from multiple providers such as Anthropic, Meta, Mistral, Cohere, and Amazon, enabling model choice, fallback, and cost/performance optimization without managing infrastructure, which supports scalability. Option B is not a Bedrock best practice because deploying models on self-managed EC2 instances moves you away from Bedrock's serverless, managed scaling and shifts patching, scaling, and security responsibilities to you. Option C is incorrect because hardcoding API keys in source code exposes credentials to leakage via repositories and logs; you should use AWS Secrets Manager or IAM roles instead.

Answer

Deploy models on EC2 instances for better control

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Store API keys in source code for easy access

Question 3

Which TWO are benefits of using Amazon SageMaker JumpStart for foundation models? (Choose 2)

Accepted Answer

Built-in fine-tuning scripts and notebooks. Option A is correct because SageMaker JumpStart provides built-in fine-tuning scripts and example notebooks that let you adapt foundation models to your own data without writing the training pipeline from scratch. Option D is correct because JumpStart includes a catalog of pre-trained foundation models (e.g., from providers like Hugging Face, Meta, and AI21) that you can deploy or customize directly. Option B is not correct because fine-tuning still requires code or configuration, such as selecting hyperparameters and preparing datasets, so it is not fully no-code. Option C is not correct because automatic scaling still requires configuring endpoint settings like instance counts and autoscaling policies. Option E is not correct because model usage in JumpStart is billed through SageMaker resources and is not free or unlimited.

Answer

No coding required to fine-tune models

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Automatic scaling without any configuration

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Free unlimited usage for all models

Question 4

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?

Accepted Answer

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

Switch to a larger foundation model like Claude 3 Opus

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The model's temperature is set too high; reduce it to 0.1

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The maximum tokens limit is too low; increase it to 4096

Question 5

A company uses Amazon Bedrock Agents to build an agent that interacts with users through a chat interface. The agent is configured with a knowledge base containing product documentation. Sometimes the agent fails to answer simple questions like 'What is your return policy?' and instead says it cannot find the answer. The knowledge base does contain the return policy. What is the most likely reason?

Accepted Answer

Simplify and clarify the agent's instruction prompt to emphasize knowledge base usage. The agent's instruction prompt might be too complex or not explicitly directing the agent to use the knowledge base. Simplifying the prompt to clearly instruct the agent to first search the knowledge base can resolve the issue. Increasing timeout or adding more data is unnecessary. A stronger model may help but is not the root cause.

Answer

Increase the agent's maximum timeout for processing

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Use a more powerful foundation model for reasoning

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Add more documents to the knowledge base

Question 6

A financial services firm fine-tuned a generative AI model on Amazon SageMaker to summarize quarterly reports. The summaries often miss key financial metrics such as revenue and profit margins. The fine-tuning dataset contained full reports with summaries that included these metrics. The model appears to understand the reports but omits critical numbers. Which course of action would most likely improve the summaries?

Accepted Answer

Re-fine-tune using a carefully crafted dataset that includes explicit instructions to include key metrics and provides examples of correct summaries. The model's failure to include key financial metrics despite having them in the training data indicates a misalignment between the training objective and the desired output. By re-fine-tuning with a dataset that explicitly instructs the model to include key metrics and provides correct examples, you directly teach the model to prioritize and extract those specific numerical values during summarization, addressing the root cause of omission.

Answer

Increase the maximum number of tokens in the summary

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Switch to a different pre-trained model like Claude instead of the current one

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Implement a post-processing Lambda function that extracts metrics from the original report and appends them to the summary

Question 7

A startup is building a customer support chatbot using Amazon Bedrock with the Claude foundation model. The chatbot needs to answer questions based on a knowledge base of frequently asked questions (FAQs) stored in an Amazon S3 bucket. The team wants to implement Retrieval Augmented Generation (RAG) to provide accurate and context-aware responses. They are evaluating different approaches to integrate the knowledge base. What is the most efficient way to implement RAG with Bedrock?

Accepted Answer

Use Amazon Bedrock Knowledge Bases to directly connect the S3 bucket and retrieve relevant documents for the prompt.. Amazon Bedrock Knowledge Bases is a fully managed RAG capability that natively connects to Amazon S3 (and other data sources), automatically chunks and embeds documents into a vector store, and retrieves the most relevant passages at query time to augment the prompt. This eliminates the need to build custom retrieval pipelines, manage embeddings, or manually inject documents. It is the most efficient, purpose-built approach for RAG on Bedrock.

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Use AWS Lambda to fetch documents from S3 and inject them into the prompt.

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Manually extract all FAQs and include them in the prompt each time the chatbot responds.

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Fine-tune the Claude model on the FAQs so the model memorizes the knowledge base.

Question 8

A financial services company wants to generate personalized investment recommendations using a large language model via Amazon Bedrock. They have customer data that includes risk tolerance, portfolio holdings, and financial goals. The company is highly concerned about data privacy and must avoid exposing sensitive personally identifiable information (PII) to the model. They plan to use a foundation model to generate recommendations based on customer profiles. What is the best approach to protect customer privacy while still enabling personalization?

Accepted Answer

Preprocess the customer data to replace sensitive fields with placeholders, then use the processed data in the prompt.. Preprocessing customer data to replace sensitive fields with placeholders (e.g., using synthetic IDs) allows the model to generate personalized recommendations without accessing real PII. This minimizes risk. Option A is incorrect because fine-tuning on a large dataset of generic recommendations does not produce personalized outputs for individual customers. Option B is incorrect because prompt engineering instructions are not a robust privacy control and cannot reliably prevent PII exposure. Option D is incorrect because including customer data directly in the prompt and relying on the model to anonymize it is unreliable and may still leak PII.

Answer

Fine-tune the model on a large dataset of investment recommendations without any customer-specific data.

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Use prompt engineering to instruct the model to disregard any personally identifiable information.

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Include the customer data directly in the prompt and rely on the model to anonymize it.

Question 9

A research lab is using Amazon SageMaker to fine-tune a large language model (LLM) for scientific text summarization. The training dataset contains 10 million documents, and the lab has a limited budget but needs to minimize training time. They have access to SageMaker Training with managed spot instances, which offer significant cost savings but are interruptible. The team is considering different training strategies to balance cost, time, and model quality. Which strategy should they use?

Accepted Answer

Use SageMaker's distributed training with data parallelism on multiple managed spot instances, and enable checkpointing.. The best strategy. Using SageMaker's distributed training with data parallelism across multiple managed spot instances allows parallel processing of the 10-million-document dataset, significantly reducing training time. Spot instances offer cost savings of up to 90% compared to on-demand, and enabling checkpointing ensures that if any instance is interrupted, training can resume from the last checkpoint without losing progress. Option B is incorrect because fine-tuning only the last few layers on a subset of data compromises model quality and does not leverage the full dataset. Option C is incorrect because a single on-demand instance is expensive and slower than distributed spot instances. Option D is incorrect because training from scratch on a single GPU is prohibitively slow and costly, especially given the budget constraints.

Answer

Fine-tune only the last few layers of the model on a smaller subset of the data.

Answer

Use a single on-demand instance to avoid interruptions and maximize throughput.

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Use a single large GPU instance to train the model from scratch.

Question 10

Which TWO of the following are key advantages of using Amazon Bedrock for building generative AI applications?

Accepted Answer

Ability to fine-tune models using your own data without managing underlying infrastructure.. Option B is correct because Amazon Bedrock is a fully managed service that lets you customize (fine-tune) foundation models with your own data while AWS handles the underlying infrastructure, so you never provision or manage servers or clusters. Option E is correct because Bedrock provides a single, unified API to access multiple foundation models from different providers such as Anthropic, AI21 Labs, Cohere, Meta, Stability AI, and Amazon, which simplifies multi-model development. Option A is wrong because Bedrock does not automatically optimize prompts for all models without user intervention; prompt engineering remains the developer's responsibility. Option C is wrong because data preprocessing is still required for many use cases, especially when preparing fine-tuning datasets or cleaning inputs. Option D is wrong because generative models are probabilistic and do not guarantee identical outputs for the same prompt, even with low temperature settings.

Answer

Automatic optimization of prompts for all models without user intervention.

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Eliminates the need for any data preprocessing before model invocation.

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Guaranteed identical outputs from all models for the same prompt.

Question 11

Refer to the exhibit. A developer has attached this IAM policy to their user. When trying to invoke the Anthropic Claude v2 model using the Bedrock runtime, they receive an AccessDeniedException. Which change to the policy would resolve the issue?

Accepted Answer

Change the Resource to arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-v2. The IAM policy's Resource element must specify the exact ARN of the foundation model being invoked. The original policy likely used a wildcard or incorrect ARN pattern. The correct ARN for Anthropic Claude v2 in us-east-1 is arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-v2, which grants permission to invoke that specific model via the Bedrock runtime.

Answer

Add the bedrock:InvokeModelWithResponseStream action

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Change the Action to bedrock:ListFoundationModels

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Remove the Resource element and set Effect to Deny

Question 12

A company operates a customer support chatbot that uses Amazon Bedrock with a knowledge base sourced from an S3 bucket containing frequently updated product documentation. The knowledge base uses OpenSearch Serverless as the vector store and is configured to sync daily. The chatbot uses the RetrieveAndGenerate API with a custom Lambda function that applies a system prompt instructing the model to base answers solely on the retrieved context. After a major update to the product documentation, the IT team verifies that the data source sync completed successfully and the new chunks are present in the OpenSearch index. However, the chatbot continues to respond with outdated information. Further investigation reveals that the Lambda function includes a response caching mechanism using Amazon ElastiCache for Redis with a Time-To-Live (TTL) of 24 hours. The cache key is based on the user query. The team notes that no cache invalidation is performed after documentation updates. What is the most likely cause of the outdated responses?

Accepted Answer

The ElastiCache cache is returning stale cached responses that contain the old information.. The Lambda function caches responses in ElastiCache for Redis with a 24-hour TTL keyed on the user query, and no invalidation occurs after documentation updates. Even though the knowledge base sync succeeded and new chunks are in OpenSearch, the chatbot returns the cached stale answer for any query previously cached. This is the classic cache-staleness failure mode.

Answer

The 'maximum results' parameter in the RetrieveAndGenerate API is set to a value too low to retrieve the new chunks.

Answer

The embedding model used by the knowledge base has not been retrained on the new documentation.

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The IAM role for the Lambda function lacks permissions to access the new S3 objects.

Question 13

A media company wants to add a generative AI assistant to its internal knowledge portal. The assistant must answer employee questions using only the company's private policy documents, and it must cite the exact source passage for each answer. The team plans to use a foundation model hosted in Amazon Bedrock. Which approach should they implement to meet these requirements?

Accepted Answer

Use Retrieval Augmented Generation by storing policy documents in a vector store and retrieving relevant passages to include in the prompt sent to the model.. The requirements are grounding answers in private documents and citing exact source passages. Retrieval Augmented Generation satisfies both by indexing the policy corpus, retrieving the most relevant chunks at query time, and passing them into the prompt so the model can answer from supplied evidence. The application can then surface the retrieved passages as citations, keeping answers accurate and auditable without retraining the foundation model.

Answer

Raise the maximum token limit in the model invocation parameters so the entire policy library can be pasted into every prompt.

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Fine-tune the foundation model on the full set of policy documents and deploy a custom model that answers from its updated weights.

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Increase the model's temperature setting so it produces more varied and detailed answers from its pretrained knowledge.

Question 14

A media company wants to build a generative AI assistant that drafts scripts and answers questions about its own style guide. The team has no machine learning engineers and wants to avoid managing GPU infrastructure or training any models. Which approach BEST describes how they should build this solution?

Accepted Answer

Use a foundation model through Amazon Bedrock and supply the style guide as context at inference time.. A pretrained foundation model delivered through a managed service removes the need to train models or operate GPU infrastructure, and the proprietary style guide can be injected as prompt context so outputs follow house rules. This combination satisfies the no-ML-team constraint while still producing grounded, task-specific generative output.

Answer

Build a rules-based template engine that reassembles stored sentences from previous scripts.

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Deploy an Amazon SageMaker endpoint hosting an open-source model and manually patch the underlying EC2 instances.

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Collect a labeled dataset and train a new transformer from scratch on Amazon SageMaker training jobs.

Question 15

A solutions architect is explaining the concept of a foundation model to a non-technical stakeholder. The stakeholder asks what distinguishes a foundation model from a traditional task-specific machine learning model. Which statement best describes a foundation model?

Accepted Answer

It is a large model pre-trained on broad data that can be adapted to many downstream tasks.. A foundation model is characterized by large-scale pre-training on broad data, which yields general capabilities that can be adapted to numerous downstream tasks via prompting, fine-tuning, or other techniques. This generality is what differentiates it from task-specific models built for a single narrow purpose, and it is the essence the architect needs to convey.

Answer

It is a model trained exclusively on a company's proprietary data for one specific use case.

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It is a small model optimized to run only on edge devices with limited compute.

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It is a rule-based system that follows hand-coded logic to produce deterministic outputs.