A university recently digitized a collection of archival documents, academic journals, and manuscripts. The university stores the digital files in an AWS Lake Formation data lake.
The university hires a GenAI developer to build a solution to allow users to search the digital files by using text queries. The solution must return journal abstracts that are semantically similar to a user's query. Users must be able to search the digitized collection based on text and metadata that is associated with the journal abstracts. The metadata of the digitized files does not contain keywords. The solution must match similar abstracts to one another based on the similarity of their text. The data lake contains fewer than 1 million files.
Which solution will meet these requirements with the LEAST operational overhead?

1. A. Use Amazon Titan Embeddings in Amazon Bedrock to create vector representations of the digitized file
2. B. Store embeddings in the OpenSearch Neural plugin for Amazon OpenSearch Service.
3. C. Use Amazon Comprehend to extract topics from the digitized file
4. D. Store the topics and file metadata in an Amazon Aurora PostgreSQL databas
5. E. Query the abstract metadata against the data in the Aurora database.
6. F. Use Amazon SageMaker AI to deploy a sentence-transformer mode
7. G. Use the model to create vector representations of the digitized file
8. H. Store embeddings in an Amazon Aurora PostgreSQL database that has the pgvector extension.
9. I. Use Amazon Titan Embeddings in Amazon Bedrock to create vector representations of the digitized file
10. J. Store embeddings in an Amazon Aurora PostgreSQL Serverless database that has the pgvector extension.

Correct Answer: D

A company uses Amazon Bedrock to build a Retrieval Augmented Generation (RAG) system. The RAG system uses an Amazon Bedrock Knowledge Bases that is based on an Amazon S3 bucket as the data source for emergency news video content. The system retrieves transcripts, archived reports, and related documents from the S3 bucket.
The RAG system uses state-of-the-art embedding models and a high-performing retrieval setup. However, users report slow responses and irrelevant results, which cause decreased user satisfaction. The company notices that vector searches are evaluating too many documents across too many content types and over long periods of time.
The company determines that the underlying models will not benefit from additional fine- tuning. The company must improve retrieval accuracy by applying smarter constraints and wants a solution that requires minimal changes to the existing architecture.
Which solution will meet these requirements?

1. A. Enhance embeddings by using a domain-adapted model that is specifically trained on emergency news content for improved vector similarity.
2. B. Migrate to Amazon OpenSearch Servic
3. C. Use vector fields and metadata filters to define the scope of results retrieval.
4. D. Enable metadata-aware filtering within the Amazon Bedrock knowledge base by indexing S3 object metadata.
5. E. Migrate to an Amazon Q Business index to perform structured metadata filtering and document categorization during retrieval.

Correct Answer: C

A company is building a generative AI (GenAI) application that processes financial reports and provides summaries for analysts. The application must run two compute environments. In one environment, AWS Lambda functions must use the Python SDK to analyze reports on demand. In the second environment, Amazon EKS containers must use the JavaScript SDK to batch process multiple reports on a schedule. The application must maintain conversational context throughout multi-turn interactions, use the same foundation model (FM) across environments, and ensure consistent authentication.
Which solution will meet these requirements?

1. A. Use the Amazon Bedrock InvokeModel API with a separate authentication method for each environmen
2. B. Store conversation states in Amazon DynamoD
3. C. Use custom I/O formatting logic for each programming language.
4. D. Use the Amazon Bedrock Converse API directly in both environments with a common authentication mechanism that uses IAM role
5. E. Store conversation states in Amazon ElastiCach
6. F. Create programming language-specific wrappers for model parameters.
7. G. Create a centralized Amazon API Gateway REST API endpoint that handles all model interactions by using the InvokeModel AP
8. H. Store interaction history in application process memory in each Lambda function or EKS containe
9. I. Use environment variables to configure model parameters.
10. J. Use the Amazon Bedrock Converse API and IAM roles for authenticatio
11. K. Pass previous messages in the request messages array to maintain conversational contex
12. L. Use programming language-specific SDKs to establish consistent API interfaces.

Correct Answer: D

An ecommerce company is building an internal platform to develop generative AI applications by using Amazon Bedrock foundation models (FMs). Developers need to select models based on evaluations that are aligned to ecommerce use cases. The platform must display accuracy metrics for text generation and summarization in dashboards. The company has custom ecommerce datasets to use as standardized evaluation inputs.
Which combination of steps will meet these requirements with the LEAST operational overhead? (Select TWO.)

1. A. Import the datasets to an Amazon S3 bucke
2. B. Provide appropriate IAM permissions and cross-origin resource sharing (CORS) permissions to give the evaluation jobs access to the datasets.
3. C. Import the datasets to an Amazon S3 bucke
4. D. Provide appropriate IAM permissions and a VPC endpoint configuration to give the evaluation jobs access to the datasets.
5. E. Configure an AWS Lambda function to create model evaluation jobs on a schedule in the Amazon Bedrock consol
6. F. Provide the URI of the S3 bucket that contains the datasets as an inpu
7. G. Configure the evaluation jobs to measure the real world knowledge (RWK) score for text generation and BERTScore for summarizatio
8. H. Configure a second Lambda function to check the status of the jobs and publish custom logs to Amazon CloudWatc
9. I. Create a custom Amazon CloudWatch Logs Insights dashboard.
10. J. Use Amazon SageMaker Clarify on a schedule to create model evaluation job
11. K. Useopen source frameworks to create and run standardized evaluation
12. L. Publish results to Amazon CloudWatch namespace
13. M. Use an AWS Lambda function to check the status of the jobs and publish custom logs to Amazon CloudWatc
14. N. Create a custom Amazon CloudWatch Logs Insights dashboard.
15. O. Run an Amazon SageMaker AI notebook job on a schedule by using the fmvelos or ragas framework to run evaluations that use the datasets in the S3 bucke
16. P. Write Python code in the notebook that makes direct InvokeModel API calls to the FMs and processes their responses for evaluatio
17. Q. Publish job status and results to Amazon CloudWatch Logs to measure the real world knowledge (RWK) score for text generation and toxicity for summarization as metrics for accurac
18. R. Create a custom CloudWatch Logs Insights dashboard.

Correct Answer: BC

A company developed a multimodal content analysis application by using Amazon Bedrock. The application routes different content types (text, images, and code) to specialized foundation models (FMs).
The application needs to handle multiple types of routing decisions. Simple routing based on file extension must have minimal latency. Complex routing based on content semantics requires analysis before FM selection. The application must provide detailed history and support fallback options when primary FMs fail.
Which solution will meet these requirements?

1. A. Configure AWS Lambda functions that call Amazon Bedrock FMs for all routing logi
2. B. Use conditional statements to determine the appropriate FM based on content type and semantics.
3. C. Create a hybrid solutio
4. D. Handle simple routing based on file extensions in application cod
5. E. Handle complex content-based routing by using an AWS Step Functions state machine with JSONata for content analysis and the InvokeModel API for specialized FMs.
6. F. Deploy separate AWS Step Functions workflows for each content type with routing logic in AWS Lambda function
7. G. Use Amazon EventBridge to coordinate between workflows when fallback to alternate FMs is required.
8. H. Use Amazon SQS with different SQS queues for each content typ
9. I. Configure AWS Lambda consumers that analyze content and invoke appropriate FMs based on message attributes by using Amazon Bedrock with an AWS SDK.

Correct Answer: B