AIF-C01 Fundamentals of Generative AI Practice Test 3 — 15 Questions

Question 1

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?

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

Accepted Answer

Refine the prompt with more descriptive language and adjust the CFG scale and inference steps.. 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.

Answer

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

Answer

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

Answer

Use a different foundation model and increase the image resolution.

Question 2

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.

Answer

Amazon CloudWatch Logs for prompt logging.

Answer

Amazon Virtual Private Cloud (VPC) for network isolation.

Answer

AWS Identity and Access Management (IAM) policies.

Question 3

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.

Answer

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

Answer

Use more detailed prompts with explicit instructions to avoid speculation.

Answer

Increase the temperature parameter to make outputs more conservative.

Question 4

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.

Answer

Increase the top_k parameter.

Answer

Increase the temperature parameter.

Answer

Increase the top_p parameter.

Question 5

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 programmatically scan both the prompt inputs and the model responses for PII patterns (for example, using regex or Amazon Comprehend's PII detection APIs) and redact or block them before they reach the user, giving the company full control over what data enters and leaves the generative AI application. Option D is correct because Amazon Bedrock Guardrails provides a native, purpose-built sensitive information filter that can detect and either block or mask PII entities (such as names, emails, and SSNs) in both user inputs and model outputs, directly satisfying the requirement to prevent PII from appearing in generated content. Option B is not correct because IAM policies only control who can invoke which Bedrock models and actions; they do not inspect or filter the content of prompts and responses for PII. Option C is not correct because CloudWatch Logs only captures and stores model outputs for auditing and monitoring after the fact; it does not prevent PII from being generated or returned. Option E is not correct because placing the Bedrock endpoint in a private VPC only affects network isolation and traffic routing, not the content-level detection or redaction of PII in model outputs.

Answer

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

Answer

Enable Amazon CloudWatch Logs to capture and audit model outputs.

Answer

Place the Bedrock model endpoint within a private VPC.

Question 6

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 techniques such as LoRA freeze most of the pretrained model weights and train only small low-rank adapter matrices, which drastically reduces GPU memory consumption, training time, and compute cost while preserving model quality. Option D is correct because SageMaker managed spot training can cut training costs by up to 90% versus on-demand instances, and pairing it with checkpointing to Amazon S3 lets training resume from the last checkpoint after a spot interruption rather than restarting from scratch. Option E is correct because mixed precision training with FP16 reduces memory footprint and leverages GPU tensor cores for faster matrix math, speeding up training and allowing larger effective batch sizes with minimal impact on model accuracy. Option A is not correct because simply moving to a larger multi-GPU instance raises cost and does not by itself optimize time or cost efficiency, and may be unnecessary if PEFT and FP16 already fit the workload. Option C is not correct because maximizing batch size to the GPU memory limit is not universally beneficial; overly large batches can hurt convergence and model quality and may require extensive hyperparameter retuning, so it is not a reliable cost-and-time optimization strategy.

Answer

Use a larger instance type with more GPUs.

Answer

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

Question 7

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 are purpose-built to detect and block harmful or inappropriate content (e.g., hate, violence, prompt injection) in both prompts and model responses, directly addressing misuse monitoring and prevention. Option C is correct because AWS CloudTrail records all Bedrock API activity and management events, providing an auditable trail of who invoked which model, when, and from where, which is essential for detecting misuse. Option E is correct because enabling model invocation logging sends full request/response data to Amazon CloudWatch Logs, allowing continuous monitoring, alerting, and forensic analysis of model inputs and outputs. Option A is not appropriate because MFA applies to human console sign-ins, not programmatic API calls, and Bedrock APIs use IAM credentials/signatures rather than MFA tokens. Option D is not required for misuse monitoring; a private VPC endpoint improves network isolation but does not detect or log misuse, and Bedrock endpoints are AWS-managed services accessed via VPC endpoints rather than being 'placed' in a VPC.

Answer

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

Answer

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

Question 8

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.

Answer

Reduce the batch size

Answer

Increase the learning rate

Answer

Increase the number of training epochs

Question 9

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.

Answer

Amazon SageMaker

Answer

Amazon Comprehend

Answer

AWS Lambda

Question 10

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.

Answer

Fine-tune the model on a curated safe dataset

Answer

Improve prompt engineering with more specific instructions

Answer

Use AWS WAF to filter API responses

Question 11

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.

Answer

AWS CloudHSM

Answer

Amazon Macie

Answer

AWS Key Management Service (AWS KMS)

Question 12

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.

Answer

Reinforcement Learning from Human Feedback (RLHF)

Answer

Training a new model from scratch on the domain data

Answer

Using prompt engineering to provide context

Question 13

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 (Licensing and usage terms) is correct because foundation models on SageMaker come with varying licenses (e.g., Apache 2.0, Llama Community License, or proprietary commercial terms), and the team must verify the license permits their intended commercial code-generation use case before deployment. Option B (Cost per token for inference) is correct because SageMaker endpoints bill based on instance hours and/or token throughput, so understanding per-token inference cost is essential for budgeting a production code-generation workload at scale. Option C (Context window length) is correct because code generation often requires ingesting large files or repositories, and a model's maximum context window (e.g., 4K, 8K, 32K, or 128K tokens) directly determines how much source code can be passed in a single prompt. Option D is not a primary selection criterion because the training algorithm (e.g., RLHF, SFT) is an internal implementation detail that does not directly affect deployment fit or task suitability. Option E is not a primary criterion because model size and architecture are secondary considerations already reflected in measurable factors like cost, latency, and context window, rather than being a distinct decision driver for this scenario.

Answer

The training algorithm used

Answer

Model size and architecture

Question 14

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.

Answer

topP set to 0.9

Answer

stopSequences is empty

Answer

maxTokenCount set to 100

Question 15

A media company is using Amazon Bedrock to generate captions for images. They have a batch processing pipeline that sends thousands of images daily to the Bedrock API using the Titan Image Generator G1 model. Recently, they started receiving ThrottlingException errors during peak hours. The team needs to process all images within 24 hours without changing the model or the application code. The current account has a default quota of 10 requests per second (RPS) for the Titan model in us-east-1. The team estimates they need 50 RPS during peak hours. They have already implemented exponential backoff in the client, but the errors persist. What is the MOST effective solution to resolve the throttling issue?

Accepted Answer

Request a service quota increase for the InvokeModel API for the Titan model in us-east-1. The team has already implemented exponential backoff, but the errors persist because their current quota of 10 RPS is insufficient for the required 50 RPS. Requesting a service quota increase for the InvokeModel API for the Titan Image Generator G1 model in us-east-1 directly addresses the root cause by raising the throughput limit, allowing the existing application code and model to handle the peak load without any architectural changes.

Answer

Use Amazon SageMaker batch transform to process images offline

Answer

Distribute the requests across multiple AWS Regions

Answer

Switch to a different foundation model that has a higher default quota