AWS CloudTrail is a service that enables governance, compliance, and operational and risk auditing of your AWS account. It tracks API calls and identifies unauthorized access attempts to AWS resources, including Amazon Bedrock.

AWS CloudTrail:

Provides detailed logs of all API calls made within an AWS account, including those to Amazon Bedrock.

Can identify unauthorized access attempts by logging and monitoring the API calls, which helps in setting appropriate IAM policies and roles.

Why Option B is Correct:

Monitoring and Security: CloudTrail logs all access requests and helps detect unauthorized access attempts.

Auditing and Compliance: The logs can be used to audit user activity and enforce security measures.

Why Other Options are Incorrect:

A. AWS Audit Manager: Used for automating audit preparation, not for tracking real-time unauthorized access attempts.

C. Amazon Fraud Detector: Designed to detect fraudulent online activities, not unauthorized access to AWS services.

D. AWS Trusted Advisor: Provides best practice recommendations for AWS resources, not access monitoring.

Thus, B is the correct answer for identifying unauthorized users attempting to access Amazon Bedrock.

Diffusion models are state-of-the-art generative models for creating high-quality, realistic images from textual prompts or other forms of conditioning. These are the foundational technology behind tools like Amazon Bedrock Titan Image Generator and other generative image models.

Generative AI models (such as large language models) are designed to generate new content, such as text, summaries, images, and more. Summarizing text—like customer complaints—is a classic application of generative AI.

A is correct:

"Text summarization is a core generative AI use case, as it involves generating new, concise content from a larger body of text."

(Reference: AWS Generative AI Use Cases)

B and C are standard ML classification/segmentation tasks.

D is a regression/prediction task, not generative.

The company wants to fine-tune an ML model on Amazon Bedrock using sensitive data stored in private databases within a VPC, ensuring the data remains within its private network. AWS PrivateLink provides a secure, private connection between a VPC and AWS services like Amazon Bedrock, allowing data to stay within the company’s network without traversing the public internet. This meets the requirement for maintaining data privacy during fine-tuning.

Exact Extract from AWS AI Documents:

From the AWS Bedrock User Guide:

"AWS PrivateLink enables you to securely connect your VPC to Amazon Bedrock without exposing data to the public internet. This is particularly useful for fine-tuning models with sensitive data, as it ensures that data remains within your private network."

(Source: AWS Bedrock User Guide, Security and Networking)

Detailed Explanation:

Option A: Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) service role.While IAM service roles control access to Amazon Bedrock, they do not address the requirement of keeping data within the private network during data transfer. This option is insufficient.

Option B: Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) resource policy.IAM resource policies define permissions for Bedrock resources but do not ensure that data stays within the private network. This option is incorrect.

Option C: Use AWS PrivateLink to connect the VPC and Amazon Bedrock.This is the correct answer. AWS PrivateLink creates a secure, private connection between the VPC and Amazon Bedrock, ensuring that sensitive data does not leave the private network during fine-tuning, as required.

Option D: Use AWS Key Management Service (AWS KMS) keys to encrypt the data.While AWS KMS can encrypt data, encryption alone does not guarantee that data remains within the private network during transfer. This option does not fully meet the requirement.

Amazon Bedrock Studio provides tools to build and manage generative AI applications, and the company needs a component to secure the content generated by AI systems. Guardrails in Amazon Bedrock are designed to ensure safe and responsible AI outputs by filtering harmful or inappropriate content, making them the key component for securing generated content.

Exact Extract from AWS AI Documents:

From the AWS Bedrock User Guide:

"Guardrails in Amazon Bedrock provide mechanisms to secure the content generated by AI systems by filtering out harmful or inappropriate outputs, such as hate speech, violence, or misinformation, ensuring responsible AI usage."

(Source: AWS Bedrock User Guide, Guardrails for Responsible AI)

Detailed Explanation:

Option A: Access controlsAccess controls manage who can use or interact with the AI system but do not directly secure the content generated by the system.

Option B: Function callingFunction calling enables AI models to interact with external tools or APIs, but it is not related to securing generated content.

Option C: GuardrailsThis is the correct answer. Guardrails in Amazon Bedrock secure generated content by filtering out harmful or inappropriate material, ensuring safe outputs.

Option D: Knowledge basesKnowledge bases provide data for AI models to generate responses but do not inherently secure the content that is generated.

Inference is the correct answer because it is the AI process that occurs when a deployed model analyzes new data (such as an image) to make predictions or identify objects.

Inference:

In the context of machine learning, inference is the process of using a trained model to make predictions on new, unseen data.

When the deep learning model is deployed to production and receives a new image for analysis, it uses the learned patterns from the training phase to identify objects in the image. This is known as inference.

Why Option B is Correct:

Inference Process: Involves applying the trained model to real-world data (the new image) to identify objects.

Deployment Context: The model has already been trained, and the deployment to production indicates it is being used for inference.

Why Other Options are Incorrect:

A. Training: Refers to the process of teaching the model using historical data, not making predictions on new data.

C. Model deployment: Refers to the process of making a trained model available for use in production.

D. Bias correction: Is a process to adjust a model to minimize bias, not for analyzing new images.

Amazon Bedrock is a fully managed service that provides access to foundation models (FMs) from various providers, enabling users to build and scale generative AI applications. It simplifies the process of integrating FMs into applications for tasks like text generation, chatbots, and more.

Exact Extract from AWS AI Documents:

From the AWS Bedrock User Guide:

"Amazon Bedrock is a fully managed service that makes foundation models (FMs) from leading AI providers available through a single API, enabling developers to build and scale generative AI applications with ease."

(Source: AWS Bedrock User Guide, Introduction to Amazon Bedrock)

Detailed Explanation:

Option A: Amazon Q DeveloperAmazon Q Developer is an AI-powered assistant for coding and AWS service guidance, not a service for hosting or providing foundation models.

Option B: Amazon BedrockThis is the correct answer. Amazon Bedrock provides access to foundation models, making it the primary service for building and scaling generative AI applications.

Option C: Amazon KendraAmazon Kendra is an intelligent search service powered by machine learning, not a service for providing foundation models or building generative AI applications.

Option D: Amazon ComprehendAmazon Comprehend is an NLP service for text analysis tasks like sentiment analysis, not for providing foundation models or supporting generative AI.