AIF-C01 Fundamentals of Generative AI — Timed 50 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.

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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.

Question 11

A company is building a chatbot using Amazon Bedrock. They want to ensure the model's responses are grounded in their internal knowledge base and avoid generating information outside that scope. Which feature should they use?

Accepted Answer

Amazon Bedrock Knowledge Bases. Amazon Bedrock Knowledge Bases is the correct feature because it allows you to connect a foundation model (FM) to your internal data sources, such as documents or databases, and use Retrieval Augmented Generation (RAG) to ground responses in that specific knowledge. This ensures the chatbot only generates information from the provided knowledge base, preventing hallucinations or out-of-scope content.

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Agents for Amazon Bedrock

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Model Evaluation on Amazon Bedrock

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Guardrails for Amazon Bedrock

Question 12

A data scientist is fine-tuning a large language model on Amazon SageMaker for a text summarization task. The training loss decreases steadily but the validation loss starts increasing after a few epochs. What should the scientist do to address this issue?

Accepted Answer

Use early stopping based on validation loss. The validation loss increasing while training loss decreases is a classic sign of overfitting. Early stopping based on validation loss halts training when the validation loss stops improving, preventing overfitting and saving computational resources. This is a standard technique in SageMaker's built-in training algorithms and custom training scripts.

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Reduce the batch size

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Increase the learning rate

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Increase the number of training epochs

Question 13

A startup wants to generate product descriptions from a few keywords using a foundation model. They need a fully managed serverless solution that requires no infrastructure setup. Which AWS service should they use?

Accepted Answer

Amazon Bedrock. Amazon Bedrock is a fully managed serverless service that provides access to foundation models (FMs) from leading AI providers via a simple API, making it ideal for generating product descriptions from keywords without any infrastructure management. It directly supports generative AI tasks like text generation, unlike other AWS services that focus on different ML or NLP capabilities.

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Amazon SageMaker

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Amazon Comprehend

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AWS Lambda

Question 14

A developer is using the Amazon Bedrock API to generate text. They notice that the model sometimes returns harmful content despite setting safety parameters. What is the BEST way to add an additional layer of content filtering?

Accepted Answer

Configure content filters in Amazon Bedrock Guardrails. Amazon Bedrock Guardrails provides a dedicated, configurable content filtering layer that can block harmful content at inference time, independent of the model's built-in safety parameters. This allows developers to enforce custom policies (e.g., hate speech, violence) without modifying the model itself, making it the best additional safeguard.

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Fine-tune the model on a curated safe dataset

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Improve prompt engineering with more specific instructions

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Use AWS WAF to filter API responses

Question 15

A team is deploying a generative AI model for medical report generation. They must ensure patient data privacy and comply with HIPAA. Which AWS service feature is essential for de-identifying protected health information (PHI) before sending data to a foundation model?

Accepted Answer

Amazon Comprehend Medical. Amazon Comprehend Medical is the correct service because it is specifically designed to extract and de-identify protected health information (PHI) from unstructured medical text using natural language processing (NLP). It can detect entities such as patient names, dates, and medical record numbers, and then redact or replace them before the data is sent to a foundation model, ensuring HIPAA compliance.

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AWS CloudHSM

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Amazon Macie

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AWS Key Management Service (AWS KMS)

Question 16

A company wants to use a pre-trained generative AI model to analyze customer feedback. They need to adjust the model for their specific domain without retraining from scratch. Which approach is MOST suitable?

Accepted Answer

Fine-tuning the model on domain-specific data. Fine-tuning is the most suitable approach because it takes a pre-trained generative AI model and updates its weights using a smaller, domain-specific dataset (e.g., customer feedback transcripts). This allows the model to adapt to the company's specific terminology, sentiment patterns, and context without the massive computational cost and data requirements of training from scratch. It preserves the general language understanding from pre-training while specializing the model for the target domain.

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Reinforcement Learning from Human Feedback (RLHF)

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Training a new model from scratch on the domain data

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Using prompt engineering to provide context

Question 17

A company is using Amazon Bedrock to generate creative marketing copy. They want to reduce the randomness of the output while maintaining diversity. Which TWO parameters should they adjust?

Accepted Answer

Decrease the top_p value. Decreasing the temperature (Option E) reduces randomness by lowering the probability of sampling lower-ranked tokens, making the model more deterministic. Decreasing top_p (Option D) narrows the cumulative probability threshold for token selection, which also reduces randomness while still allowing some diversity within the narrowed set. Together, these parameters control the trade-off between creativity and determinism in Amazon Bedrock's text generation.

Answer

Increase the temperature

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Increase the max token count

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Increase the top_k value

Question 18

A data science team is evaluating foundation models for a code generation task. They need a model that is fine-tuned for code and can be deployed on Amazon SageMaker. Which THREE criteria are important to consider when selecting a model?

Accepted Answer

Licensing and usage terms. Option A is correct because licensing and usage terms directly impact whether a foundation model can be legally used for commercial code generation. Models like Code Llama or StarCoder have specific licenses (e.g., Llama 2 Community License, OpenRAIL-M) that may restrict fine-tuning, redistribution, or use in proprietary products. Ignoring these terms could lead to compliance violations when deploying on Amazon SageMaker.

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Cost per token for inference

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The training algorithm used

Question 19

A developer attached this IAM policy to a role used by an application that invokes Claude v2 in us-east-1. The application receives an access denied error. What is the MOST likely cause?

Accepted Answer

The Deny statement is blocking requests because the condition does not match the resource ARN's region. The Deny statement uses a `StringNotEquals` condition on `aws:RequestedRegion` set to `us-east-1`. This means the Deny applies to any request where the requested region is NOT `us-east-1`. Since the resource ARN in the Deny statement is `arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-v2`, the condition does not match the resource's region (the resource ARN itself is in us-east-1), but the Deny is triggered when the request is made to a different region, blocking the call. The application is likely invoking the model from a region other than us-east-1, causing the Deny to take effect.

Answer

The Allow statement does not include a condition on the region

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The Deny statement uses StringNotEquals instead of StringEquals

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The resource ARN in the Allow statement is incorrect

Question 20

A developer is using the Amazon Bedrock InvokeModel API with the above request to summarize meeting notes. The response is a single word repeated many times. Which parameter is MOST likely causing this issue?

Accepted Answer

temperature set to 0. A temperature of 0 forces the model to always select the highest-probability token at each step, which can lead to repetitive loops if the most likely token repeatedly points back to itself (e.g., the same word). This deterministic behavior eliminates randomness, causing the model to get stuck in a single-word cycle rather than generating diverse or coherent text.

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topP set to 0.9

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stopSequences is empty

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maxTokenCount set to 100