AIF-C01 Fundamentals of Generative AI — Timed 10 Questions

Question 1

A company is building a chatbot using Amazon Bedrock and wants to ensure that the model generates responses consistent with its brand voice. Which technique should be used to provide the model with examples of desired responses without fine-tuning the model?

Accepted Answer

Include few-shot examples in the system prompt to demonstrate the desired tone.. Option D is correct because few-shot prompting allows you to provide the model with examples of desired responses directly in the system prompt, guiding the model's tone and style without modifying its underlying weights. This technique is ideal for brand voice consistency when fine-tuning is not an option, as it leverages in-context learning to influence output behavior.

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Fine-tune the model on a dataset of brand-compliant conversations.

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Use prompt chaining to break down the conversation into multiple steps.

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Implement a Retrieval Augmented Generation (RAG) system with brand documents.

Question 2

A data scientist is using Amazon SageMaker to train a large language model from scratch. Which AWS service is most suitable for managing the training infrastructure, including automatic scaling and spot instance recovery?

Accepted Answer

Amazon SageMaker Training job.. Amazon SageMaker Training jobs are the most suitable service for managing training infrastructure because they provide built-in automatic scaling, managed spot instance recovery, and distributed training orchestration. This allows the data scientist to focus on model development rather than provisioning and managing EC2 instances, load balancers, or recovery scripts.

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AWS Lambda function.

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Amazon SageMaker Notebook instance.

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Amazon EC2 with a custom setup.

Question 3

A team is using Amazon Bedrock to generate images from text prompts. The generated images often contain artifacts and do not match the prompt description. Which combination of steps should the team take to improve image quality?

Accepted Answer

Refine the prompt with more descriptive language and adjust the CFG scale and inference steps.. Option C is correct because refining the prompt with more descriptive language helps the model better interpret the user's intent, while adjusting the CFG (Classifier-Free Guidance) scale controls how strictly the model adheres to the prompt, and increasing inference steps allows the diffusion process to produce higher-quality, artifact-free images. These are standard hyperparameters in diffusion-based image generation models on Amazon Bedrock, directly addressing both artifacts and prompt mismatch.

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Fine-tune the model using SageMaker Ground Truth and increase the training epochs.

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Increase the max token count and use a larger model variant.

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Use a different foundation model and increase the image resolution.

Question 4

A developer is creating a generative AI application using Amazon Bedrock and needs to ensure that responses do not include toxic or harmful content. Which feature should be enabled?

Accepted Answer

Amazon Bedrock Guardrails.. Amazon Bedrock Guardrails is the correct feature because it is specifically designed to enforce content policies, filter toxic or harmful content, and block undesirable topics in generative AI responses. It provides configurable thresholds for hate, insults, sexual content, violence, and other harmful categories, ensuring compliance with safety requirements without modifying the underlying model.

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Amazon CloudWatch Logs for prompt logging.

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Amazon Virtual Private Cloud (VPC) for network isolation.

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AWS Identity and Access Management (IAM) policies.

Question 5

A company is using Amazon SageMaker JumpStart to deploy a pre-trained text generation model. After deployment, the model produces slow inference responses. Which action is most likely to improve inference latency?

Accepted Answer

Deploy the model on a more powerful instance type with higher GPU memory.. Option B is correct because deploying the model on a more powerful instance type with higher GPU memory directly addresses the computational bottleneck causing slow inference. A larger GPU provides more CUDA cores and memory bandwidth, enabling faster matrix operations and reducing the time per forward pass for the pre-trained text generation model.

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Quantize the model weights to FP16 or INT8.

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Fine-tune the model on a smaller dataset.

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Increase the batch size for inference requests.

Question 6

An organization is using Amazon Bedrock to power a customer service chatbot. They notice that the chatbot occasionally generates hallucinated information about product specifications. Which strategy should be implemented to reduce hallucinations?

Accepted Answer

Integrate a Retrieval Augmented Generation (RAG) system with the product catalog.. Retrieval Augmented Generation (RAG) grounds the model's responses in authoritative, up-to-date product catalog data, directly reducing hallucinations by ensuring the chatbot references verified facts rather than relying solely on its parametric memory. This is the most effective strategy because it provides a retrieval-based factual foundation that fine-tuning or prompt engineering alone cannot guarantee.

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Fine-tune the model on a dataset of product specification conversations.

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Use more detailed prompts with explicit instructions to avoid speculation.

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Increase the temperature parameter to make outputs more conservative.

Question 7

A developer is using Amazon Bedrock's Claude model to summarize long documents. The developer notices that the summaries sometimes miss key points. Which parameter adjustment is most likely to improve summary completeness?

Accepted Answer

Increase the max_tokens parameter.. Increasing max_tokens allows the model to generate longer outputs, which is essential when summarizing long documents because the summary may need more tokens to capture all key points. If max_tokens is too low, the model truncates the response, potentially omitting important details. This directly addresses the issue of missing key points by providing sufficient output length for a complete summary.

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Increase the top_k parameter.

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Increase the temperature parameter.

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Increase the top_p parameter.

Question 8

A company is building a generative AI application using Amazon Bedrock and needs to ensure that the model does not generate outputs containing personally identifiable information (PII). Which TWO actions should the company take? (Choose 2)

Accepted Answer

Implement a custom AWS Lambda function to scan and redact PII from inputs and outputs.. Option A is correct because a custom AWS Lambda function can be integrated into the application workflow to programmatically scan and redact PII from both inputs and outputs before they reach or leave the Bedrock model. This provides a flexible, code-driven approach to data sanitization, allowing the use of libraries like Amazon Comprehend or regex patterns to detect and mask PII entities such as names, addresses, and social security numbers.

Answer

Use AWS Identity and Access Management (IAM) policies to restrict model access.

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Enable Amazon CloudWatch Logs to capture and audit model outputs.

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Place the Bedrock model endpoint within a private VPC.

Question 9

A research team is using Amazon SageMaker to fine-tune a large language model. They want to optimize training cost and time without sacrificing model quality. Which THREE strategies should they implement? (Choose 3)

Accepted Answer

Apply parameter-efficient fine-tuning (PEFT) techniques like LoRA.. Option B is correct because Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA (Low-Rank Adaptation) freeze the pre-trained model weights and inject trainable rank decomposition matrices into specific layers. This drastically reduces the number of trainable parameters (often by 10,000x), lowering memory and compute requirements while preserving model quality, making it ideal for cost- and time-sensitive fine-tuning.

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Use a larger instance type with more GPUs.

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Increase the batch size to the maximum that fits in GPU memory.

Question 10

A company is deploying a generative AI model on Amazon Bedrock and needs to monitor for potential misuse. Which THREE measures should they implement? (Choose 3)

Accepted Answer

Configure Amazon Bedrock Guardrails to block harmful content.. Option B is correct because Amazon Bedrock Guardrails provides configurable content filters that can block harmful or undesirable content in both input prompts and model responses. This is a direct monitoring and prevention mechanism for misuse, allowing administrators to define policies for topics, toxicity, and sensitive information.

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Require multi-factor authentication (MFA) for all API calls.

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Place the Bedrock endpoint in a private VPC with no internet access.