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Amazon AWS Certified AI Practitioner Sample Questions (Q67-Q72):
NEW QUESTION # 67
A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality.
Which action must the company take to use the custom model through Amazon Bedrock?
Answer: C
Explanation:
To use a custom model that has been trained to improve summarization quality, the company must deploy the model on an Amazon SageMaker endpoint. This allows the model to be used for real-time inference through Amazon Bedrock or other AWS services. By deploying the model in SageMaker, the custom model can be accessed programmatically via API calls, enabling integration with Amazon Bedrock.
Option B (Correct): "Deploy the custom model in an Amazon SageMaker endpoint for real-time inference": This is the correct answer because deploying the model on SageMaker enables it to serve real-time predictions and be integrated with Amazon Bedrock.
Option A: "Purchase Provisioned Throughput for the custom model" is incorrect because provisioned throughput is related to database or storage services, not model deployment.
Option C: "Register the model with the Amazon SageMaker Model Registry" is incorrect because while the model registry helps with model management, it does not make the model accessible for real-time inference.
Option D: "Grant access to the custom model in Amazon Bedrock" is incorrect because Bedrock does not directly manage custom model access; it relies on deployed endpoints like those in SageMaker.
AWS AI Practitioner Reference:
Amazon SageMaker Endpoints: AWS recommends deploying models to SageMaker endpoints to use them for real-time inference in various applications.
NEW QUESTION # 68
A financial institution is using Amazon Bedrock to develop an AI application. The application is hosted in a VPC. To meet regulatory compliance standards, the VPC is not allowed access to any internet traffic.
Which AWS service or feature will meet these requirements?
Answer: A
Explanation:
AWS PrivateLink enables private connectivity between VPCs and AWS services without exposing traffic to the public internet. This feature is critical for meeting regulatory compliance standards that require isolation from public internet traffic.
* Option A (Correct): "AWS PrivateLink": This is the correct answer because it allows secure access to Amazon Bedrock and other AWS services from a VPC without internet access, ensuring compliance with regulatory standards.
* Option B: "Amazon Macie" is incorrect because it is a security service for data classification and protection, not for managing private network traffic.
* Option C: "Amazon CloudFront" is incorrect because it is a content delivery network service and does not provide private network connectivity.
* Option D: "Internet gateway" is incorrect as it enables internet access, which violates the VPC's no- internet-traffic policy.
AWS AI Practitioner References:
* AWS PrivateLink Documentation: AWS highlights PrivateLink as a solution for connecting VPCs to AWS services privately, which is essential for organizations with strict regulatory requirements.
NEW QUESTION # 69
Which component of Amazon Bedrock Studio can help secure the content that AI systems generate?
Answer: B
Explanation:
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 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.
Reference:
AWS Bedrock User Guide: Guardrails for Responsible AI (https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails.html) AWS AI Practitioner Learning Path: Module on Responsible AI and Model Safety Amazon Bedrock Developer Guide: Securing AI Outputs (https://aws.amazon.com/bedrock/)
NEW QUESTION # 70
A student at a university is copying content from generative AI to write essays.
Which challenge of responsible generative AI does this scenario represent?
Answer: D
NEW QUESTION # 71
An airline company wants to build a conversational AI assistant to answer customer questions about flight schedules, booking, and payments. The company wants to use large language models (LLMs) and a knowledge base to create a text-based chatbot interface.
Which solution will meet these requirements with the LEAST development effort?
Answer: B
Explanation:
The airline company aims to build a conversational AI assistant using large language models (LLMs) and a knowledge base to create a text-based chatbot with minimal development effort. Retrieval Augmented Generation (RAG) on Amazon Bedrock is an ideal solution because it combines LLMs with a knowledge base to provide accurate, contextually relevant responses without requiring extensive model training or custom development. RAG retrieves relevant information from a knowledge base and uses an LLM to generate responses, simplifying the development process.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Retrieval Augmented Generation (RAG) in Amazon Bedrock enables developers to build conversational AI applications by combining foundation models with external knowledge bases. This approach minimizes development effort by leveraging pre-trained models and integrating them with data sources, such as FAQs or databases, to provide accurate and contextually relevant responses." (Source: AWS Bedrock User Guide, Retrieval Augmented Generation) Detailed Explanation:
* Option A: Train models on Amazon SageMaker Autopilot.SageMaker Autopilot is designed for automated machine learning (AutoML) tasks like classification or regression, not for building conversational AI with LLMs and knowledge bases. It requires significant data preparation and is not optimized for chatbot development, making it less suitable.
* Option B: Develop a Retrieval Augmented Generation (RAG) agent by using Amazon Bedrock.
This is the correct answer. RAG on Amazon Bedrock allows the company to use pre-trained LLMs and integrate them with a knowledge base (e.g., flight schedules or FAQs) to build a chatbot with minimal effort. It avoids the need for extensive training or coding, aligning with the requirement for least development effort.
* Option C: Create a Python application by using Amazon Q Developer.While Amazon Q Developer can assist with code generation, building a chatbot from scratch in Python requires significant development effort, including integrating LLMs and a knowledge base manually, which is more complex than using RAG on Bedrock.
* Option D: Fine-tune models on Amazon SageMaker Jumpstart.Fine-tuning models on SageMaker Jumpstart requires preparing training data and customizing LLMs, which involves more effort than using a pre-built RAG solution on Bedrock. This option is not the least effort-intensive.
References:
AWS Bedrock User Guide: Retrieval Augmented Generation (https://docs.aws.amazon.com/bedrock/latest
/userguide/rag.html)
AWS AI Practitioner Learning Path: Module on Generative AI and Conversational AI Amazon Bedrock Developer Guide: Building Conversational AI (https://aws.amazon.com/bedrock/)
NEW QUESTION # 72
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