Question 1

A legal technology company is building a generative AI application to draft contract clauses. The application must utilize a repository of 200,000 proprietary legal documents that is updated with new case files every night.The Chief Legal Officer has set the following strict requirements:Attribution: Every generated clause must include a direct citation to the specific case file it was derived from.Freshness: The model must incorporate data from documents added to the repository within the last 24 hours.Accuracy: The solution must minimize hallucinations and strictly adhere to the facts present in the proprietary documents.Which architectural strategy should the GenAI engineer implement to meet these requirements?

Options :

A : Use Amazon Bedrock Custom Models to fine-tune a base Large Language Model (LLM) on the proprietary documents. Configure an automated pipeline using Amazon SageMaker Pipelines to re-train and deploy a new model version every night.

B : Perform Continued Pre-training on an Amazon Titan model using the proprietary documents to adapt the model to the legal domain. Use the resulting custom model to generate clauses based on its internal knowledge of the dataset.

C : Use Amazon SageMaker JumpStart to deploy an open-source model to a real-time inference endpoint. Implement a LangChain workflow that loads the entire document dataset into the model's context window for each inference request.

D : Implement a Retrieval Augmented Generation (RAG) architecture using Amazon Bedrock Knowledge Bases. Configure the data source to sync with the document repository daily and set the generation configuration to return source citations.

Answer: D

Question 2

A healthcare technology company is building a patient inquiry chatbot using Amazon Bedrock. Before logging conversation transcripts for quality assurance, they must ensure that all personally identifiable information (names, phone numbers, addresses, medical record numbers) is detected and redacted to comply with HIPAA privacy requirements.Which combination of AWS services should the developer use to detect and redact PII from conversation logs?

Options :

A : Use AWS CloudTrail to automatically redact PII from all logs

B : Use Amazon Textract to extract PII from documents, then delete the documents

C : Use Amazon Comprehend to detect PII entities, then use Amazon Bedrock Guardrails to redact them from logs

D : Use Amazon Macie to scan conversation logs stored in S3

Answer: C

Question 3

A legal tech company uses Amazon Bedrock to extract specific liability clauses from uploaded contracts. Users have reported instances where the model "hallucinates" clauses that do not exist in the source text.Which mechanisms should the developer implement to verify accuracy and reduce these hallucinations?

Options :

A : Define a JSON Schema representing the expected liability data structure and include it in the model instructions to enforce constrained, deterministic output.

B : Implement a post-processing workflow that calculates the semantic similarity score between the generated response and the source text, rejecting responses that fall below a defined confidence threshold.

C : Configure Amazon Bedrock Guardrails with a "Sensitive Information" filter to redact names and addresses found in the contract clauses.

D : Increase the inference temperature parameter to 0.9 and enable Top-P sampling to broaden the diversity of the generated clauses.

E : Use Amazon CloudWatch to monitor the InvocationLatency and ModelTokenCount metrics to identify anomalies in the model's processing speed.

Answer: A,B

Question 4

A development team is building a serverless application where AWS Lambda functions need to invoke Amazon Bedrock foundation models to generate product descriptions. The security team requires that the Lambda functions follow the principle of least privilege when accessing AWS services.Which approach should the developer implement to grant the Lambda function access to Amazon Bedrock?

Options :

A : Embed AWS access keys and secret keys as environment variables in the Lambda function configuration

B : Grant the Lambda function's IAM role full Administrator access to ensure it can call all AWS services

C : Attach an IAM role to the Lambda function with the bedrock:InvokeModel permission for the specific models being used

D : Store IAM credentials in the Lambda function code and rotate them manually every 90 days

Answer: C

Question 5

A media company is building a content recommendation engine that uses Amazon Bedrock embeddings to find similar articles based on semantic meaning. They need a fully managed vector database that can efficiently perform k-nearest neighbor (k-NN) searches across millions of article embeddings. The team wants a service that integrates natively with their existing search infrastructure and supports both keyword and vector search.Which AWS service should the team use as their vector database?

Options :

A : Amazon ElastiCache

B : Amazon DynamoDB

C : Amazon RDS PostgreSQL

D : Amazon OpenSearch Service

Answer: D

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