A company wants to develop a large language model (LLM) application by using Amazon Bedrock and customer data that is uploaded to Amazon S3. The company's security policy states that each team can access data for only the team's own customers.

Which solution will meet these requirements?

A. Create an Amazon Bedrock custom service role for each team that has access to only the team's customer data.

B. Create a custom service role that has Amazon S3 access. Ask teams to specify the customer name on each Amazon Bedrock request.

C. Redact personal data in Amazon S3. Update the S3 bucket policy to allow team access to customer data.

D. Create one Amazon Bedrock role that has full Amazon S3 access. Create IAM roles for each team that have access to only each team's customer folders.

A company is building a chatbot to improve user experience. The company is using a large language model (LLM) from Amazon Bedrock for intent detection. The company wants to use few-shot learning to improve intent detection accuracy.

Which additional data does the company need to meet these requirements?

A. Pairs of chatbot responses and correct user intents

B. Pairs of user messages and correct chatbot responses

C. Pairs of user messages and correct user intents

D. Pairs of user intents and correct chatbot responses

A medical company deployed a disease detection model on Amazon Bedrock. To comply with privacy policies, the company wants to prevent the model from including personal patient information in its responses. The company also wants to receive notification when policy violations occur.

Which solution meets these requirements?

A. Use Amazon Macie to scan the model's output for sensitive data and set up alerts for potential violations.

B. Configure AWS CloudTrail to monitor the model's responses and create alerts for any detected personal information.

C. Use Guardrails for Amazon Bedrock to filter content. Set up Amazon CloudWatch alarms for notification of policy violations.

D. Implement Amazon SageMaker Model Monitor to detect data drift and receive alerts when model quality degrades.

A company has built an image classification model to predict plant diseases from photos of plant leaves. The company wants to evaluate how many images the model classified correctly.

Which evaluation metric should the company use to measure the model's performance?

A. R-squared score

B. Accuracy

C. Root mean squared error (RMSE)

D. Learning rate

An AI practitioner trained a custom model on Amazon Bedrock by using a training dataset that contains confidential data. The AI practitioner wants to ensure that the custom model does not generate inference responses based on confidential data.

How should the AI practitioner prevent responses based on confidential data?

A. Delete the custom model. Remove the confidential data from the training dataset. Retrain the custom model.

B. Mask the confidential data in the inference responses by using dynamic data masking.

C. Encrypt the confidential data in the inference responses by using Amazon SageMaker.

D. Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS).

A company needs to build its own large language model (LLM) based on only the company's private data. The company is concerned about the environmental effect of the training process.

Which Amazon EC2 instance type has the LEAST environmental effect when training LLMs?

A. Amazon EC2 C series

B. Amazon EC2 G series

C. Amazon EC2 P series

D. Amazon EC2 Trn series

A company wants to use a large language model (LLM) to develop a conversational agent. The company needs to prevent the LLM from being manipulated with common prompt engineering techniques to perform undesirable actions or expose sensitive information.

Which action will reduce these risks?

A. Create a prompt template that teaches the LLM to detect attack patterns.

B. Increase the temperature parameter on invocation requests to the LLM.

C. Avoid using LLMs that are not listed in Amazon SageMaker.

D. Decrease the number of input tokens on invocations of the LLM.

A company built a deep learning model for object detection and deployed the model to production.
Which AI process occurs when the model analyzes a new image to identify objects?

A. Training

B. Inference

C. Model deployment

D. Bias correction

An AI practitioner is using an Amazon Bedrock base model to summarize session chats from the customer service department. The AI practitioner wants to store invocation logs to monitor model input and output data.

Which strategy should the AI practitioner use?

A. Configure AWS CloudTrail as the logs destination for the model.

B. Enable invocation logging in Amazon Bedrock.

C. Configure AWS Audit Manager as the logs destination for the model.

D. Configure model invocation logging in Amazon EventBridge.

A company is building a contact center application and wants to gain insights from customer conversations. The company wants to analyze and extract key information from the audio of the customer calls.

Which solution meets these requirements?

A. Build a conversational chatbot by using Amazon Lex.

B. Transcribe call recordings by using Amazon Transcribe.

C. Extract information from call recordings by using Amazon SageMaker Model Monitor.

D. Create classification labels by using Amazon Comprehend.

A law firm wants to build an AI application by using large language models (LLMs). The application will read legal documents and extract key points from the documents.

Which solution meets these requirements?

A. Build an automatic named entity recognition system.

B. Create a recommendation engine.

C. Develop a summarization chatbot.

D. Develop a multi-language translation system.

An AI practitioner wants to use a foundation model (FM) to design a search application. The search application must handle queries that have text and images.

Which type of FM should the AI practitioner use to power the search application?

A. Multi-modal embedding model

B. Text embedding model

C. Multi-modal generation model

D. Image generation model