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NEW QUESTION # 26
What are tokens in the context of generative AI models?
Answer: C
NEW QUESTION # 27
A company is building an application that needs to generate synthetic data that is based on existing data.
Which type of model can the company use to meet this requirement?
Answer: B
NEW QUESTION # 28
A company wants to develop an Al application to help its employees check open customer claims, identify details for a specific claim, and access documents for a claim. Which solution meets these requirements?
Answer: B
Explanation:
The company wants an AI application to help employees check open customer claims, identify claim details, and access related documents. Agents for Amazon Bedrock can automate tasks by interacting with external systems, while Amazon Bedrock knowledge bases provide a repository of information (e.g., claim details and documents) that the agent can query to respond to employee requests, making this the best solution.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Agents for Amazon Bedrock enable developers to build applications that can perform tasks by interacting with external systems and data sources. When paired with Amazon Bedrock knowledge bases, agents can access structured and unstructured data, such as documents or databases, to provide detailed responses for use cases like customer service or claims management." (Source: AWS Bedrock User Guide, Agents and Knowledge Bases) Detailed Option A: Use Agents for Amazon Bedrock with Amazon Fraud Detector to build the application.Amazon Fraud Detector is for detecting fraudulent activities, not for managing customer claims or accessing documents. This option is irrelevant.
Option B: Use Agents for Amazon Bedrock with Amazon Bedrock knowledge bases to build the application.This is the correct answer. Agents for Amazon Bedrock can interact with knowledge bases to retrieve claim details and documents, enabling employees to check open claims and access relevant information.
Option C: Use Amazon Personalize with Amazon Bedrock knowledge bases to build the application.Amazon Personalize is for building recommendation systems, not for retrieving claim details or documents. This option does not meet the requirements.
Option D: Use Amazon SageMaker AI to build the application by training a new ML model.Training a new ML model on SageMaker is unnecessary and complex for this use case, as the task can be efficiently handled by Agents and knowledge bases on Amazon Bedrock.
Reference:
AWS Bedrock User Guide: Agents and Knowledge Bases (https://docs.aws.amazon.com/bedrock/latest/userguide/agents.html) AWS AI Practitioner Learning Path: Module on Generative AI and Knowledge Bases Amazon Bedrock Developer Guide: Building AI Applications (https://aws.amazon.com/bedrock/)
NEW QUESTION # 29
A publishing company built a Retrieval Augmented Generation (RAG) based solution to give its users the ability to interact with published content. New content is published daily. The company wants to provide a near real-time experience to users.
Which steps in the RAG pipeline should the company implement by using offline batch processing to meet these requirements? (Select TWO.)
Answer: A,E
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In a RAG (Retrieval Augmented Generation) architecture, there are steps that can be optimized using offline batch processing, particularly for operations that do not require real-time updates:
A . Generation of content embeddings:
When new content is published, it can be processed in batches to generate embeddings (vector representations) offline. These embeddings are then used at query time for similarity search. As new documents come in daily, batch processing is ideal for generating embeddings for all new content together.
"Content/document embeddings are typically generated offline, as this operation can be computationally expensive and does not need to happen in real-time." (Reference: AWS GenAI RAG Blog, Amazon Bedrock RAG Pattern)
"Content/document embeddings are typically generated offline, as this operation can be computationally expensive and does not need to happen in real-time." (Reference: AWS GenAI RAG Blog, Amazon Bedrock RAG Pattern) C . Creation of the search index:
After generating the content embeddings, these are indexed in a vector database or search service. This indexing is also typically performed in batch as part of the offline pipeline.
"Building or updating the vector index is often performed as a batch operation, reflecting the latest state of the content repository." (Reference: AWS RAG Pattern Whitepaper)
"Building or updating the vector index is often performed as a batch operation, reflecting the latest state of the content repository." (Reference: AWS RAG Pattern Whitepaper) B, D, and E are real-time steps. Embeddings for user queries (B), retrieval of relevant content (D), and response generation (E) must be processed in real-time to provide an interactive experience.
Reference:
Retrieval Augmented Generation (RAG) on AWS
Amazon Bedrock RAG Documentation
NEW QUESTION # 30
A company has developed an ML model to predict real estate sale prices. The company wants to deploy the model to make predictions without managing servers or infrastructure.
Which solution meets these requirements?
Answer: C
Explanation:
Amazon SageMaker endpoints provide fully managed, serverless model deployment for real-time and batch predictions, allowing companies to deploy ML models without handling any servers or infrastructure management.
D is correct: SageMaker endpoints let you deploy, scale, and monitor ML models with no infrastructure overhead.
A and B require infrastructure management.
C (CloudFront/S3) is not for model deployment, but for static content delivery.
"Amazon SageMaker endpoints allow you to deploy machine learning models for inference without the need to manage underlying infrastructure." (Reference: AWS SageMaker Model Deployment, AWS Certified AI Practitioner Study Guide)
"Amazon SageMaker endpoints allow you to deploy machine learning models for inference without the need to manage underlying infrastructure." (Reference: AWS SageMaker Model Deployment, AWS Certified AI Practitioner Study Guide)
NEW QUESTION # 31
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