Fundamentals of Generative AI

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?

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?

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?

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?

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?

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?

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)

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)

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)

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?

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?

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?

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?

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?

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?

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?

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?

A company wants to generate product descriptions from a few keywords without managing infrastructure. Which AWS service provides a serverless API for accessing foundation models?

A data scientist is evaluating foundation models for a text summarization task and wants to use a standard metric. Which metric is commonly used to assess the quality of generated summaries?

A company is building a chatbot that must provide accurate answers based on internal documents without retraining the model. Which approach should they use?

A developer wants to test different foundation models quickly without setting up infrastructure. Which AWS service allows interactive prompting and comparison of multiple models?

A machine learning engineer notices that a generative AI model occasionally produces biased outputs. Which AWS feature can automatically filter harmful content before it reaches users?

A team is using Amazon Bedrock with a Claude model and wants to ensure responses adhere to a specific output format such as JSON. Which technique should be applied?

A startup wants to integrate a generative AI chatbot into their mobile app with minimal latency. Which AWS service is purpose-built for deploying foundation models with low latency and high throughput?

A company wants to personalize its generative AI model for its specific domain without sharing data with third-party model providers. Which method should they use?

A developer is using Amazon Bedrock's Converse API to build a multi-turn conversation. They notice the model forgets earlier context after a few exchanges. What is the most likely cause?

Which TWO actions are best practices for reducing hallucinations in generative AI models? (Choose 2)

Which THREE are key capabilities of Amazon Bedrock? (Choose 3)

A developer runs this AWS CLI command to invoke a model in us-west-2 but receives an error: 'An error occurred (ModelNotFoundException) when calling the InvokeModel operation: Model not found'. What is the most likely cause?

A developer deployed this guardrail to block sensitive topics and sexual content. However, the model still generates responses about a specific sensitive topic that is not in the TopicPolicy. What should the developer do to prevent this?

A retail company wants to generate product descriptions from catalog data. The data includes structured attributes (e.g., price, brand) and unstructured reviews. The team needs to ensure factual accuracy. Which approach is most appropriate?

Which AWS service provides a serverless experience for building and scaling generative AI applications with access to various foundation models?

A startup is building an AI-powered code assistant using a large language model (LLM). They want to ensure the model generates syntactically correct code and avoids security vulnerabilities. Which technique should they prioritize?

A bank is using Amazon Bedrock to summarize customer support transcripts. The summaries often contain factual inaccuracies (hallucinations). Which approach is most effective for reducing hallucinations?

A media company uses a foundation model on Amazon Bedrock to generate article summaries. The model occasionally omits important details. Which prompt engineering technique is most likely to improve completeness?

What is a foundation model?

Which TWO strategies can help reduce inference costs when using Amazon Bedrock? (Select TWO.)

Which TWO practices help ensure responsible AI when deploying generative AI applications? (Select TWO.)

Which THREE steps are typically involved in fine-tuning a foundation model? (Select THREE.)

Refer to the exhibit. A developer runs the CLI command to summarize text using Claude v2 in Bedrock. The output is shorter than expected. Which change should the developer make to allow a longer response?

Refer to the exhibit. A company sets up a knowledge base for a customer support chatbot using Amazon Bedrock. Users report that the chatbot misses relevant details from long documents. Which change to the data source configuration would most likely improve retrieval?

Refer to the exhibit. A developer receives an error when trying to invoke the Claude Instant model from an application. The application uses the IAM role 'MyAppRole'. Which IAM policy statement should be added to the role to resolve the error?

A company wants to build a generative AI application that can summarize customer support tickets. They need to ensure the model stays up-to-date with the latest product documentation without retraining. Which AWS service would best support this requirement?

A developer is testing different prompts for a text generation model on Amazon Bedrock. Which parameter controls the randomness of the model's output?

A company is using Amazon Bedrock to generate marketing copy. They want to ensure the output is safe and appropriate. Which TWO actions should they take? (Choose 2.)

A machine learning team is fine-tuning a foundation model using Amazon SageMaker. They need to optimize training time and cost. Which approach should they take?

An application uses this configuration to enable RAG. What is required for the knowledge base to function?

A developer wants to test different prompt variations for a chatbot without making repeated API calls. Which Amazon Bedrock feature can help compare model responses?

A company is deploying a customer-facing chatbot using Amazon Bedrock. They want to reduce the risk of generating biased or harmful responses. Which TWO measures should they implement? (Choose 2.)

A developer invoked an Amazon Bedrock model and received the following error: 'ValidationException: 1 validation error detected: Value 'claude-instant-v1' at 'modelId' failed to satisfy constraint: Member must satisfy enum value set: [ai21.j2-mid-v1, amazon.titan-text-lite-v1, anthropic.claude-v2, ...]'. What is the likely cause?

Which AWS service provides a fully managed experience for building generative AI applications with a variety of foundation models through a unified API?

A company is using Amazon Bedrock to generate marketing copy. They want to ensure the model's responses are factually accurate and grounded in their proprietary knowledge base. Which feature should they use?

A developer is building a chatbot using Amazon Bedrock and Claude. They notice that the model sometimes generates harmful or biased responses. Which AWS service can they use to implement guardrails?

A company wants to use a large language model to generate code based on natural language descriptions. They need to minimize latency and control costs by running inference on their own infrastructure. Which approach is most suitable?

A data scientist wants to fine-tune a foundation model on a specific domain dataset using Amazon SageMaker. Which built-in SageMaker feature can simplify the training process?

A company is using Amazon Bedrock to summarize long documents. They notice that the summary sometimes omits key details. What is the most likely cause?

An enterprise wants to ensure that generative AI applications built on AWS comply with data privacy regulations. They need to prevent the model from using customer data in future training. Which feature of Amazon Bedrock should they enable?

A developer is building an application that generates product descriptions from images using a multimodal model. Which AWS service provides access to multimodal foundation models?

A team is developing a real-time code completion feature using an LLM deployed on Amazon SageMaker. They observe high latency under load. Which optimization technique should they prioritize?

Which TWO AWS services can be used to build a chatbot that responds to customer inquiries using a company's documentation as source? (Select two.)

A company wants to evaluate the performance of a generative AI model before deployment. Which TWO metrics are most relevant for measuring model quality? (Select two.)

Refer to the exhibit. A developer is optimizing latency for a generative AI model deployed on SageMaker. Based on the exhibit, which change would most likely reduce per-token latency?

Refer to the exhibit. A developer wants to choose a model that can generate text (not just embeddings) and has the lowest cost. Based on the exhibit, which model should they select?

A developer wants to generate product description images using Amazon Bedrock. They need to ensure the generated images match a specific brand style. Which feature should they primarily use?

A financial services company is subject to strict regulatory requirements. They plan to use generative AI to summarize customer interaction logs. Which combination of AWS services and configurations best ensures compliance while maintaining accuracy?

A company is using Amazon Bedrock to generate code snippets. They notice the model occasionally generates code that fails to compile. What is the most effective way to improve code quality without retraining?

A healthcare organization wants to use generative AI to draft clinical notes from patient-physician conversations. They must comply with HIPAA and minimize false medical information. Which approach should they take?

A startup wants to quickly prototype a generative AI application for summarizing news articles. They have limited ML expertise and want minimal infrastructure management. Which AWS service should they use?

A company uses Amazon Bedrock to generate marketing content. They want to reduce costs while maintaining response quality. Which action is most effective?

A developer is using Amazon Bedrock to create a chatbot. They want to ensure the bot does not generate toxic or offensive content. Which feature should they enable?

A company operates in a region where Amazon Bedrock is not available. They want to use generative AI but must keep data within the country. Which solution should they consider?

Which TWO actions can help reduce the likelihood of hallucinations in a generative AI model used for question answering?

Which TWO factors are most important when selecting a foundation model in Amazon Bedrock for a text summarization task with strict latency requirements?

Which THREE considerations are essential when deploying a generative AI application in a regulated industry such as healthcare?

A company deployed a question-answering system using Amazon Bedrock with a knowledge base (RAG). Users report that the model often hallucinates facts not in the knowledge base. What is the most effective way to reduce hallucinations?

A developer is building a customer-facing chatbot using Amazon Bedrock. To ensure the chatbot does not generate offensive or inappropriate content, which AWS feature should they implement?

A data scientist fine-tuned a large language model on Amazon SageMaker for financial report generation. The model produces responses that are too short and incomplete, often cutting off mid-sentence. What parameter should be adjusted first?

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?

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

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

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?

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?

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?

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?

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?

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?

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

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?

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?

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?

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?

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?

A financial services firm is evaluating foundation models for a customer-facing assistant. Compliance requires that prompts and completions never leave the firm's own AWS account boundary. Which characteristic of a foundation model deployment should the team evaluate FIRST against this constraint?

A financial services company wants to deploy a generative AI assistant that answers questions using its internal policy documents. The team is concerned that the model may invent policies that do not exist. Which approach best reduces this risk while keeping the model's answers tied to the company's actual documents?

A data science intern is reading about the transformer architecture that underpins most modern large language models. The intern asks which mechanism allows a transformer to weigh the relevance of every other word in a sentence when encoding a given word, regardless of how far apart the words appear. Which mechanism should you name?

A content team wants to build an internal tool that drafts blog posts from short outlines. They have no machine learning engineers on staff and want AWS to manage the underlying model infrastructure while they focus only on prompts and output. Which AWS service should they use?

A product team wants its generative AI assistant to answer questions about internal policy documents accurately rather than from the model's general training knowledge. Which TWO techniques should they apply? (Choose two.)

A financial services company wants to deploy a generative AI assistant that summarizes internal earnings reports. The reports contain confidential, non-public financial data, and the security team requires that the data never leaves the company's AWS environment or be used to train the provider's public models. The company already uses Amazon Bedrock. Which characteristic of Amazon Bedrock directly satisfies this requirement?

A media company wants a generative AI assistant that drafts scripts in a distinctive house style. The team has several thousand pages of approved scripts but no labeled input-output pairs, and they want to adapt an existing foundation model rather than train one from scratch. Which approach best matches their data and goal?

A machine learning engineer is preparing to fine-tune a foundation model for a specialized legal summarization task. The team has only a few thousand labeled examples and a limited budget. Which fine-tuning approach is most appropriate to adapt the model efficiently under these constraints?

A startup is choosing between two foundation models for a summarization feature. Model X is a large general-purpose model with strong benchmark scores; Model Y is a smaller domain-specialized model with lower general benchmarks but excellent results on the startup's own document samples. Cost per token for Model Y is roughly one third of Model X. Which evaluation practice should the team follow?

A media company wants to create an internal tool that drafts short news summaries from press releases. They have no machine learning engineers, no labeled training data, and need a working prototype within two weeks. Which approach best describes how they should build this capability using generative AI concepts on AWS?

A solutions architect is explaining why a foundation model can answer questions about topics it was never explicitly programmed for. Which characteristic of generative AI best explains this behaviour?

A media company is experimenting with an Amazon Bedrock text model to draft short news summaries. The team notices that when they ask the same question twice, the model returns noticeably different wording each time, and sometimes the summary drifts off topic. They want more deterministic, focused responses without retraining the model. Which combination of inference parameters should they adjust to reduce randomness and keep the output on topic?

A media company is evaluating foundation models for a generative AI application that produces image captions and short video summaries. The team must balance output quality, latency, and operational cost. Which TWO considerations are most important when selecting a foundation model for this multimodal task? (Choose two.)

A financial services firm wants its generative AI assistant to answer questions using its private policy documents, which change frequently. The firm does not want to retrain the foundation model each time a document is updated. Which approach best meets these requirements?

A support organization notices its generative AI assistant sometimes invents policy details that do not exist. Leadership wants a measurable way to track whether this behavior improves over time. Which action should the team take?

A financial services firm is deploying a generative AI assistant that answers employee questions about internal policies. Compliance requires that every response cite the exact policy document and section used. The assistant currently relies only on the foundation model's pretrained knowledge and frequently invents policy details. Which technique should the team implement to ground responses in the firm's own documents and produce citations?

A solutions architect must choose a foundation model for an application that summarizes lengthy internal audit reports. The reports average 60,000 tokens, and the summaries must reflect details from the beginning, middle, and end of each document. Cost per request matters, but recall of details is the top priority. Which model characteristic should drive the selection?

A healthcare startup is building a patient-education chatbot. They want the model to answer only using an approved set of clinical guideline documents, and they must be able to update those documents weekly without retraining any model. They plan to use Amazon Bedrock. Which approach best meets these requirements?

A developer is prompting a foundation model to classify customer feedback into categories. The model sometimes returns extra commentary along with the category label, breaking downstream parsing. The developer wants more deterministic, tightly formatted output without retraining the model. Which technique best addresses this?

A media company is evaluating foundation models for an application that will summarize long earnings-call transcripts in English for internal analysts. The transcripts are up to 40,000 words, and the summaries must remain faithful to the source. Which TWO model characteristics are most important to evaluate for this workload? (Choose two.)

A product team is comparing foundation models for a customer-facing FAQ bot. They need low latency for real-time chat and want to pay only for what they use without managing servers. Which combination of model characteristic and AWS consumption model best fits these requirements?

A financial services firm is deploying a generative AI assistant that answers employee questions about internal policy documents. The security team requires that answers be traceable to source text and that the model not invent policy details. Which TWO techniques should be implemented to ground responses and reduce fabricated content? (Choose two.)

A marketing team wants to build a generative AI application that produces short promotional copy and accompanying images for product launches. They are evaluating Amazon Bedrock and want to understand which statements accurately describe its capabilities for this use case. (Choose two.)

A healthcare analytics team is evaluating whether a generative AI solution is appropriate for summarizing patient intake notes into structured clinical fields. They are concerned about the model producing confident but incorrect medical details. Which TWO practices best reduce the risk of fabricated content in this scenario? (Choose two.)

A developer sets a low temperature value when calling a foundation model through Amazon Bedrock for a use case that extracts structured fields from invoices. A colleague argues that lowering temperature reduces hallucination and guarantees correct extraction. How should the developer respond?

A product team is designing an internal tool that helps engineers understand a large legacy codebase. They want the assistant to explain functions, suggest refactors, and answer questions about dependencies. The team is comparing a general-purpose foundation model with a model pre-trained specifically on source code. Which consideration most strongly favors the code-specialized model?

A data science team is comparing two approaches for customizing a foundation model in Amazon Bedrock for a domain-specific classification task. Approach one is providing a small set of labeled examples directly in the prompt for each request. Approach two is fine-tuning the model on a larger labeled dataset. The team wants the lowest operational overhead and the fastest way to start, and their label set is small and changes frequently. Which statement best describes the trade-off they should consider?

A developer is explaining how a large language model generates text so that a business stakeholder understands why the same prompt can yield different answers. Which description accurately captures the generation process?

A hospital's IT team wants a generative AI assistant that answers patient-billing questions using only the hospital's internal policy documents, without retraining the model. Which approach should they use?

A media company's generative AI writing assistant produces fluent but sometimes fabricated statistics. The team wants to reduce these hallucinations without changing the foundation model. Which action best addresses the root cause?

A product team wants to compare two foundation models on their own customer-support transcripts before choosing one for a chatbot. They need a quantitative measure of how well each model's answers match reference answers. Which evaluation approach fits this need?

A financial services firm is evaluating foundation models for a loan-summary assistant. They must consider both model characteristics and operational constraints. Which TWO factors most directly affect whether a candidate model can be deployed to meet their requirements? (Choose two.)

A developer wants a foundation model to reliably return a structured record with fixed fields for downstream processing. The model currently returns free-form prose that breaks parsing. Which technique most directly improves output reliability for this use case?