Applications of Foundation Models

A healthcare company is using Amazon Bedrock to summarize patient notes. The compliance team requires that no patient data is used to improve the underlying foundation model. Which configuration should the team choose?

A marketing firm uses Amazon Bedrock to generate ad copy. They notice that the generated text often includes factual inaccuracies about their products. Which technique would most effectively reduce these inaccuracies?

A developer is using Amazon Bedrock to build a chatbot that answers customer queries. The chatbot must only respond based on the provided company documentation. Which approach best meets this requirement?

A financial services company is deploying a foundation model to analyze customer sentiment from call transcripts. The model outputs must be consistent and deterministic for auditing purposes. Which parameter configuration should the company use?

An e-commerce company is using a foundation model to generate product descriptions. They want to reduce costs by caching frequently requested descriptions. Which AWS service should they use to implement a cache?

A company wants to use a foundation model to automatically moderate user-generated content. The model must filter out inappropriate content with high accuracy. Which Amazon service is best suited for this task?

A startup is using Amazon Bedrock to power a virtual assistant. They need to ensure that personally identifiable information (PII) is not included in the model's responses. Which feature should they enable?

A company is using Amazon Bedrock to generate marketing content. They want to evaluate the quality of the generated text. Which TWO metrics are most appropriate for evaluating text quality?

A data scientist is fine-tuning a foundation model on Amazon Bedrock for a custom summarization task. Which THREE practices should they follow to optimize the fine-tuning process?

A company is using Amazon Bedrock to generate code snippets. They want to ensure the generated code is secure. Which TWO practices should they implement?

Refer to the exhibit. An IAM policy is attached to a user. Which models can the user invoke?

Refer to the exhibit. A user invokes Claude v2 using the AWS CLI. The response is truncated. What is the most likely cause?

A company is building a chatbot using Amazon Bedrock to answer customer questions about their product catalog. The chatbot should only use information from the company's internal knowledge base and should not generate answers based on the model's pre-training data. Which feature should be enabled?

A financial services company is using Amazon Bedrock to generate investment summaries. They want to ensure that the model outputs are factually accurate and based on the latest market data. Which combination of services should they use to achieve this? (Select TWO)

A company is using a foundation model on Amazon Bedrock to generate customer support responses. They notice that the model sometimes produces harmful or offensive content. Which approach is MOST effective to mitigate this issue?

A healthcare organization is using Amazon Bedrock to analyze medical images and generate radiology reports. They need to comply with HIPAA regulations and ensure patient data is not used for model training. Which configuration should they use?

A company wants to use a foundation model to automatically summarize lengthy documents. Which capability of foundation models is being utilized?

A developer is using Amazon Bedrock to generate code snippets. The model often produces insecure code. Which prompt engineering technique is MOST effective to improve security?

A company is using Amazon Bedrock to build a text-to-SQL application. They want to ensure that the generated SQL queries are valid and safe. Which approach is BEST?

A company is using Amazon Bedrock to generate product descriptions. They notice that the model sometimes produces descriptions that contain factual errors about the products. Which TWO actions should they take to improve factual accuracy?

A company wants to classify customer emails into categories (e.g., complaint, inquiry, feedback) using a foundation model. Which approach is MOST efficient?

A company is using Amazon Bedrock to generate marketing copy. They want to evaluate the quality of the generated text. Which metric is MOST suitable for assessing the relevance and coherence of the content?

A company is developing a chatbot using Amazon Bedrock and wants to ensure the model's responses do not include toxic or biased language. The company has a labeled dataset of undesirable responses. Which approach should be used to fine-tune the foundation model to reduce harmful outputs?

A developer is calling the Amazon Bedrock InvokeModel API to generate text with the AI21 Labs Jurassic-2 Mid model. The API call includes a maxTokens parameter, but the request fails with the error shown in the exhibit. What is the most likely cause of this error?

A company uses Amazon Bedrock to generate product descriptions. They want to ensure the outputs consistently follow a specific brand tone (professional yet friendly). They have a small set of example descriptions (few-shot examples) but do not want to fine-tune the model. Which strategy best achieves consistent tone without modifying the base model?

An e-commerce company uses a foundation model to generate personalized email subject lines. The marketing team notices that the subject lines sometimes contain product recommendations that are out of stock. Which action would best reduce the generation of out-of-stock recommendations without retraining the model?

A company wants to build a chatbot that responds to customer queries using a foundation model. They need low latency and want to avoid managing infrastructure. Which AWS service should they use?

A developer is using Amazon Bedrock to generate text summaries. The output sometimes includes irrelevant information. What is the most effective prompt engineering technique to improve relevance?

A financial services company uses a foundation model for document analysis. They need to ensure the model does not output sensitive customer information from its training data. What is the most effective mitigation?

A company wants to use a foundation model to classify customer feedback into positive, neutral, negative. They have a small labeled dataset. What approach yields best results?

An e-commerce company uses Amazon Bedrock to generate product descriptions. They notice the descriptions are too long and contain repetitive phrases. Which parameter adjustment can help?

A healthcare company needs to use a foundation model for analyzing medical records while complying with HIPAA. They plan to use Amazon Bedrock. What should they do to meet HIPAA requirements?

A developer wants to quickly experiment with multiple foundation models using a single API. Which service provides this capability?

A company uses a foundation model for real-time translation in a chat application. The latency is high. Which optimization would reduce latency the most?

A data scientist is fine-tuning a foundation model on a custom dataset using Amazon SageMaker. After training, the model shows high accuracy on training data but poor on validation. Which action should be taken?

Which THREE of the following are factors to consider when selecting a foundation model for a text generation task?

Which TWO of the following are valid methods to reduce the risk of foundation models generating harmful or biased content?

Which THREE of the following are benefits of using Amazon Bedrock for foundation models?

Refer to the exhibit. A data scientist created this endpoint config for a foundation model in Amazon SageMaker. However, the endpoint fails to scale under load. What is the most likely reason?

Refer to the exhibit. A developer deploys this CloudFormation stack but the agent fails to query the knowledge base. What is a likely cause?

Refer to the exhibit. A developer runs this command but gets an error: 'An error occurred (AccessDeniedException) when calling the ListFoundationModels operation'. What is the most likely cause?

A company uses Amazon Bedrock to generate product descriptions. They notice that the output sometimes contains incorrect information. What should they do to improve accuracy?

A healthcare company uses Amazon Bedrock to generate patient summaries. They need to ensure no protected health information (PHI) is leaked in the output. Which AWS service can they use to detect and mask PHI in text?

A company uses Amazon SageMaker JumpStart to deploy a foundation model. They want to fine-tune the model on their own dataset. Which SageMaker capability should they use?

A company uses Amazon Bedrock to build a conversational AI. They want to enforce role-based access to the model. Which AWS service should they use?

A data scientist uses Amazon Bedrock. The model responses are too long. Which parameter should they adjust to limit the output length?

A company uses Amazon Bedrock to generate code. They want to ensure the code follows security best practices and does not contain vulnerabilities. Which approach is most effective?

A company wants to use a pre-trained foundation model for sentiment analysis without any customization. Which Amazon Machine Learning service provides access to foundation models via API?

A company uses Amazon Bedrock to generate marketing copy. They want to measure the quality of generated text compared to reference text. Which metric is most appropriate?

A company uses Amazon Bedrock with a custom model deployed via Amazon SageMaker. They want to monitor for data drift in input prompts over time. Which AWS service is best suited for this?

A company is using Amazon Bedrock to generate images. They want to ensure the outputs comply with content policies. Which TWO AWS services can help? (Choose two.)

A data science team is fine-tuning a foundation model on Amazon SageMaker. Which THREE steps are part of the best practice? (Choose three.)

A company uses Amazon Bedrock to build a question-answering system. Which THREE features of Amazon Bedrock can improve answer accuracy? (Choose three.)

A data scientist runs the above AWS CLI command and receives the error. What is the most likely cause?

A security engineer creates the above IAM policy to allow a user to invoke an Amazon Bedrock model. However, invocation fails. What is the issue?

A developer invokes an Amazon Bedrock model and receives the above response. What does the 'stopReason' field indicate?

A company uses Amazon Bedrock to build a chatbot. The chatbot needs to answer questions based on internal company documents. Which AWS service should be integrated with Bedrock to enable Retrieval Augmented Generation (RAG) without managing infrastructure?

A developer is using Amazon Bedrock with the Claude model for text summarization. The output sometimes includes inaccurate information. What is the best practice to reduce hallucinations?

A company fine-tunes a foundation model on SageMaker JumpStart for sentiment analysis. After deployment, the model shows bias toward positive sentiment. Which action should be taken to mitigate bias?

A startup needs to generate product descriptions from bullet points using a foundation model. They want a fully managed serverless experience. Which AWS service should they use?

A company fine-tunes a foundation model on SageMaker using a custom dataset. They notice the training job takes too long. Which optimization technique is specifically designed to reduce training time for foundation models?

An enterprise deploys a foundation model on Amazon Bedrock with a knowledge base. Users report that the model is returning outdated information. What is the most likely cause?

A developer wants to experiment with a foundation model for code generation without writing any code. Which AWS service provides a playground for models like CodeWhisperer?

A company is using Amazon Bedrock to generate images from text prompts. They need to ensure the generated images do not contain offensive content. Which feature should be enabled?

An organization uses SageMaker JumpStart to deploy a foundation model for real-time inference. They observe high latency. What is the most effective way to reduce latency?

A company is building a chatbot using Amazon Bedrock. They want to provide up-to-date information from a continuously changing database. Which TWO services can be used as a data source for a Bedrock knowledge base? (Select TWO.)

A data scientist is fine-tuning a foundation model on SageMaker. They want to prevent overfitting. Which THREE actions can help? (Select THREE.)

Which TWO of the following are benefits of using Amazon Bedrock for foundation models compared to managing your own infrastructure? (Select TWO.)

A developer receives the above response from invoking a Bedrock model. Which field indicates that the model completed its response normally?

A developer encounters the error shown above when using Amazon Bedrock. What is the most likely cause?

A developer sends the above request to Amazon Bedrock with Anthropic Claude. The model returns a response that stops before reaching 500 tokens. What is the most likely reason?

A company uses Amazon Bedrock to generate summarizations of lengthy reports. Users report that the summaries are too verbose and include excessive detail. Which prompt engineering technique should the team apply to address this issue?

A healthcare company is deploying a conversational AI using a foundation model on Amazon Bedrock for patient triage. The application must minimize hallucinations and ensure factual accuracy. Which combination of techniques should the team implement?

A data science team is fine-tuning a Llama 2 7B model on Amazon SageMaker for a text classification task. After the first training run, they notice the loss is not decreasing and the model is overfitting to the small training set. What should the team change to mitigate overfitting?

An e-commerce company uses Amazon Bedrock to generate product descriptions from keywords. Some descriptions contain inaccurate details about product specifications. Which approach should the company take to reduce factual errors?

A media company is using Amazon Bedrock to generate marketing copy with a foundation model. They want to ensure the output adheres to brand voice guidelines (e.g., friendly, professional). Which prompt engineering strategy is most effective for this requirement?

A startup is deploying a foundation model on Amazon SageMaker for real-time inference. They notice high latency (over 2 seconds per request). Which action is most likely to reduce latency?

A financial services company is evaluating Amazon Bedrock for a compliance application that requires explainable AI decisions. The model's output must be auditable and traceable to specific reasoning. Which Bedrock feature should they use to meet this requirement?

A research team is using Amazon Bedrock to analyze scientific papers. They want the model to generate answers based only on papers published after 2023. Which approach should they use?

A company is using Amazon Bedrock to generate code snippets. Developers report that the generated code sometimes contains security vulnerabilities. Which action should the team take to mitigate this risk?

Which TWO actions are best practices when deploying foundation models on Amazon SageMaker for production? (Choose TWO.)

Which THREE are benefits of using Amazon Bedrock over self-managing foundation models on EC2? (Choose THREE.)

Which TWO techniques can reduce the cost of running a fine-tuned foundation model on Amazon SageMaker? (Choose TWO.)

A company wants to automatically summarize customer support tickets into a short paragraph. Which AWS service is MOST appropriate for this task?

A company runs a chatbot using a large language model on Amazon Bedrock. They notice high latency during peak hours. Which action would be MOST effective to reduce latency without degrading response quality?

A generative AI application occasionally produces factually incorrect responses. The team has already tried prompt engineering and increasing the temperature parameter. Which next step is MOST effective to improve factual accuracy?

A startup needs to build a real-time text translation feature for a customer chat application. Latency must be under 200 ms per request. Which AWS approach is BEST suited?

A healthcare company processes patient records using a foundation model on Amazon Bedrock. They must ensure no patient data is used to improve the base model. What is the MOST effective configuration?

A team is fine-tuning a foundation model using SageMaker. They want to minimize training time while keeping the model's original knowledge. Which technique is BEST suited?

A data scientist wants to quickly experiment with a pre-trained LLM for text generation without writing any code. Which AWS service is MOST suitable?

A team deployed a text generation model on Amazon Bedrock. They want to monitor for toxic content in model outputs. Which evaluation approach is MOST effective?

A company is building a multi-modal application that processes images and text to answer questions about product defects. Which foundation model approach is BEST?

Which TWO factors are MOST important when selecting a foundation model for a text summarization task? (Choose two.)

Which TWO actions can help reduce bias in a foundation model’s outputs? (Choose two.)

Which THREE practices are recommended for responsible AI when deploying foundation models? (Choose three.)

Refer to the exhibit. This is an Amazon Bedrock invocation request for Claude. What is the purpose of the "stop_sequences" parameter?

Refer to the exhibit. The training job is failing with an error 'CUDA out of memory'. Which hyperparameter change is MOST likely to resolve the issue?

Refer to the exhibit. A developer sees this error when calling Amazon Bedrock for inference. What is the MOST likely cause and recommended solution?

A company is building a customer support chatbot using Amazon Bedrock. They need to store conversation history for context across sessions. Which AWS service is best suited for this purpose?

A company uses Amazon Bedrock to generate marketing copy. The summaries are too verbose. Which parameter should be decreased to directly limit the length of the output?

A company uses Amazon Bedrock to generate product descriptions. They need to ensure outputs do not contain offensive language. Which service should they integrate to filter content?

A developer is building a RAG-based Q&A bot with Amazon Bedrock Knowledge Bases. They need a managed vector store for document embeddings. Which service should they use?

Which pricing model does Amazon Bedrock use for foundation model inference?

A company wants to adapt a foundation model for a custom domain with very limited labeled data and minimal cost. Which approach is most suitable?

A developer uses Amazon Bedrock to generate code. Some outputs contain syntax errors. What is the most likely cause?

Which AWS service provides a serverless API for accessing foundation models with per-token pricing?

Which parameter controls the randomness of generated text in a foundation model?

Which TWO actions are recommended for improving the factual accuracy of a foundation model's responses when using RAG?

Which THREE are best practices for ensuring generated content complies with corporate brand guidelines when using Amazon Bedrock?

Which TWO AWS services can be used together to build a chatbot that leverages a foundation model for natural language understanding?

Refer to the exhibit. You receive this response from Amazon Bedrock. What is the most likely cause of the incomplete information?

Refer to the exhibit. You are trying to invoke a foundation model via Amazon Bedrock but receive this error. What should you do to resolve it?

A company operates a customer service platform that uses Amazon Bedrock with a foundation model to generate automated responses. The system has been in production for three months. Recently, customers have reported that responses are becoming repetitive and less relevant over time. The development team notices that the model's performance has degraded, especially for queries about newer products that were added after the initial deployment. The team currently uses a static prompt with a fixed knowledge base that was set up at launch. The model is invoked via the Bedrock API with standard settings. The team wants to improve response quality without incurring high costs or extensive re-engineering. What should the team do?

A company needs to summarize thousands of customer reviews daily using a foundation model. The solution must minimize latency and cost while handling variable traffic. Which AWS service should they use?

A development team uses a foundation model via Amazon Bedrock to generate code snippets. They notice that the model sometimes produces code with security vulnerabilities, such as SQL injection. The team wants to reduce these occurrences without delaying project timelines. What should they do?

A legal firm wants to use a foundation model to extract key clauses from thousands of contracts. Accuracy is critical, and the model must not hallucinate or fabricate information. The firm has a large internal database of labeled contracts. Which approach should they take?

A startup uses Amazon Bedrock with a provisioned throughput to generate product images. They now have unpredictable traffic and want to reduce costs. What should they do?

A company wants to build a customer service chatbot using a foundation model. The chatbot must respond in under 2 seconds and handle high throughput. Which model deployment option should they choose?

A financial services company needs to use a foundation model for sensitive data analysis. They require that all data remains within a VPC and no data leaves the AWS network. Which solution should they choose?

Which TWO of the following are benefits of using Amazon Bedrock for building applications with foundation models?

A data scientist is using a foundation model to summarize long documents. Which TWO of the following steps are most likely to improve the quality of the summaries?

A marketing team is using a foundation model to generate marketing copy. Which THREE of the following should they consider to ensure responsible and cost-effective use?

A company uses Amazon Bedrock to automatically generate product descriptions for their e-commerce website. They use a prompt that includes product attributes and a short description as a starting point. Recently, the generated descriptions have become overly verbose, including irrelevant details and sometimes even incorrect product specifications. The team has tried simplifying the prompt and reducing the max tokens, but the issue persists. The descriptions must be concise and accurate. What is the most effective next step to address this problem?

A data science team fine-tuned a foundation model on Amazon SageMaker for sentiment analysis of customer reviews. They deployed the model as a real-time endpoint. After a successful launch, the application experienced a surge in traffic, and the endpoint's latency increased from 200ms to over 2 seconds. The team needs to reduce latency and maintain high throughput without increasing costs significantly. They are using a single ml.g5.xlarge instance. What change should the team make first?

A company uses Amazon Bedrock to power a generative chatbot for employee onboarding. Recently, some employees reported that the chatbot occasionally provides responses that contain biased or offensive language. The company has a strict policy for respectful communication. They want to implement a solution quickly without retraining the model. Which action should they take?

A multinational corporation uses a foundation model via Amazon Bedrock to translate internal communication documents from English to multiple languages. They notice that the translations often miss company-specific jargon and acronyms, leading to confusion. The company has a glossary of approved translations for terms like 'Project Atlas' and 'Operation Synergy.' They want to improve translation accuracy quickly and with minimal effort. What approach should they take?

A marketing agency uses a foundation model to generate images for social media campaigns. Some generated images have contained violent or inappropriate content, damaging the brand. The agency needs to prevent such content from being displayed automatically. They are using Amazon Bedrock for image generation with Stable Diffusion. What is the most effective way to filter out inappropriate images?

A law firm uses a foundation model to draft legal briefs. To ensure accuracy, they want to ground the model's outputs in authoritative legal sources. They have a large database of prior case law and statutes stored in Amazon S3. The firm's IT team must implement a solution that reduces hallucinations while being cost-effective. The solution should allow the model to retrieve relevant documents and generate responses based on them. Which approach should they take?

A company is building a chatbot using Amazon Bedrock. They want to ensure the model's responses are grounded in company-specific data and that harmful content is filtered out. Which two services or features should they use? (Choose TWO.)

A startup company is developing an e-commerce platform and wants to use Amazon Bedrock to generate product descriptions automatically. They have a small team of developers who are not machine learning experts. The product catalog is stored in a DynamoDB table, and each product has attributes like name, category, price, and a brief description. The company wants the generated descriptions to reflect the unique brand voice, which is documented in a few internal style guides stored as PDF files in Amazon S3. They need a solution that allows them to quickly test the approach without significant infrastructure changes or model training. The development team is familiar with AWS SDKs and want to minimize ongoing maintenance. The team has already set up a Bedrock foundation model (Claude) and can make API calls. They tested simple prompts but the output lacked the brand's informal yet professional tone. They want to incorporate examples from the style guides directly into the prompt without retraining. The team fears that including the entire style guide in each prompt would exceed token limits and increase costs. Which approach should they take to effectively incorporate the brand voice with minimal changes?

A large enterprise uses Amazon Bedrock to power a conversational agent that handles customer service inquiries. The agent is built using Bedrock Agents and retrieves information from a knowledge base that contains product documentation and FAQs. Recently, users have reported that the agent sometimes provides incorrect information that contradicts the knowledge base. The development team verified that the knowledge base contains accurate and up-to-date data. They also confirmed that the retrieval process correctly fetches relevant documents. However, the agent occasionally ignores the retrieved context and generates plausible-sounding but incorrect answers. The team is concerned about customer trust and wants to improve the accuracy of the agent's responses without overhauling the architecture. They have already tuned the prompt template to instruct the model to use the context. The issue persists. Which additional action should the team take to reduce the number of hallucinated responses?

A company uses Amazon Bedrock to generate code snippets for internal tools. They notice that the generated code often contains security vulnerabilities such as SQL injection and cross-site scripting. The security team has compiled a comprehensive list of secure coding guidelines and examples of vulnerable patterns. The development team wants to reduce vulnerabilities without significantly slowing down the code generation process. They have tried adding the guidelines to the system prompt, but the model still produces insecure code occasionally. The team is considering additional measures. Which action should they take to most effectively eliminate security vulnerabilities in the generated code?

A financial services company is deploying a foundation model on Amazon Bedrock to generate compliance reports from internal audit logs. The model must not output any personally identifiable information (PII). They have configured a Bedrock Guardrail with sensitive information filters set to the 'HIGH' sensitivity level. During testing in a staging environment, testers still observed PII being occasionally generated in the report outputs. The guardrail did not block these instances because the PII was embedded in a context that the guardrail's pattern matching did not catch (e.g., structured JSON data with embedded names). The company requires a solution that minimizes latency and cost, as they process thousands of reports daily. They cannot afford to increase inference time significantly due to strict SLAs. They also want to avoid re-engineering the entire solution. Which additional step should they take to effectively eliminate PII leakage while maintaining performance?

A developer is trying to invoke the Claude v2 model in Amazon Bedrock from a Lambda function. The Lambda function's IAM role has the following policy attached: { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": "bedrock:InvokeModel", "Resource": "*" } ] } When the Lambda function runs, it receives the error shown in the exhibit. Which additional step is most likely needed to resolve this issue?

A company is deploying a customer service chatbot using a large language model (LLM) via Amazon Bedrock. The application must meet high accuracy for domain-specific queries, low latency, and be cost-effective. Which TWO strategies should the company adopt to achieve these goals? (Choose two.)