Question 1

An online gaming platform is launching a GenAI-powered support assistant using multiple Foundation Models (FMs) available in Amazon Bedrock (including Amazon Titan and Anthropic Claude). The security team has issued strict requirements for the assistant's input handling:PII Protection: Users must be prevented from submitting email addresses or phone numbers.Toxicity: Hate speech and insults must be blocked immediately.Custom Blocking: A specific list of known "cheat codes" and "exploit keywords" must be blocked from entering the model context.Consistency: These rules must apply uniformly across all used FMs without rewriting application logic for each model.Which solution should you implement to meet these requirements with the LEAST operational overhead?

Options :

A : Develop a pre-processing AWS Lambda function using the Python re library to detect PII patterns and cheat codes. Use Amazon Comprehend DetectSentiment API to identify hate speech. Configure the application to call this Lambda function and verify the result before calling InvokeModel on Amazon Bedrock.

B : Implement a standardized system prompt across all FMs that instructs the models to refuse to answer if the input contains emails, phone numbers, hate speech, or words from the cheat code list. Use the Amazon Bedrock Converse API to ensure the system prompt is strictly enforced by the model’s reasoning engine.

C : Use AWS WAF (Web Application Firewall) attached to the application load balancer handling the chat traffic. Create custom regex rules to block PII patterns and cheat codes. Enable the AWS Managed Rules for Anonymous IP List to reduce toxicity risks from bad actors.

D : Create an Amazon Bedrock Guardrail. Configure PII filters to block emails and phone numbers, and enable Content filters for hate speech. Use the Word filters feature to upload the list of cheat codes and exploit keywords. Associate the Guardrail with the InvokeModel API calls for all used FMs.

Answer: D

Question 2

A smart-home device manufacturer wants to use a Large Language Model (LLM) to identify usage trends from millions of daily device logs. Each log contains a specific device ID, the user's exact GPS coordinates, and a timestamped list of voice commands. Privacy regulations require that the manufacturer must not share granular location data or linkable user IDs with the analytics model, but the model still needs to understand regional usage patterns (e.g., "users in Northern Europe use heating more often").Which data pipeline architecture effectively prepares this data for the model while maintaining regulatory compliance?

Options :

A : Ingest the raw logs into an Amazon S3 bucket and use Amazon Athena to create a view that filters out columns containing Device IDs. Point the foundation model to the S3 bucket backing the Athena view and trust the model's system prompt to ignore any remaining location data.

B : Store the raw logs in Amazon S3 and enable S3 Object Lock in compliance mode to prevent modification. Use Amazon SageMaker Data Wrangler to feed the raw data directly into the model for fine-tuning, ensuring that the training job is run within a VPC without internet access.

C : Create an AWS Lambda function that hashes the Device ID and generalizes the GPS coordinates to a city or region level. Write these transformed logs to a separate "Analytics" S3 bucket and grant the foundation model access only to this secondary bucket for ingestion.

D : Implement a sidecar container in the ingestion pipeline that encrypts the GPS coordinates using asymmetric encryption. Pass the encrypted logs to the foundation model and provide the private key only within the RAG retrieval logic so the model can decrypt locations on-the-fly for regional analysis.

Answer: C

Question 3

A healthcare company stores patient intake forms, insurance documents, and medical records as PDF files in multiple Amazon S3 buckets. Before building a GenAI document summarization system, the security team needs to identify which S3 buckets contain sensitive PII (like Social Security numbers, patient names, and medical record numbers) to ensure proper access controls are in place.Which AWS service should the security team use to automatically discover and classify sensitive data across their S3 buckets?

Options :

A : Amazon Inspector

B : Amazon Macie

C : Amazon Comprehend

D : AWS CloudTrail

Answer: B

Question 4

A financial services company has 15 development teams building different microservices using Amazon Bedrock. Code reviews reveal inconsistent error handling patterns, varying retry logic implementations, and different approaches to streaming responses across services. The architecture team defined standard Bedrock integration patterns, but manual enforcement through code reviews is slow and inconsistent. The company needs a solution that automatically identifies non-standard implementations and helps developers refactor code to match approved patterns across the entire multi-repository codebase.Which solution enforces consistent integration patterns MOST effectively?

Options :

A : Create an AWS CodeCommit repository with approved code templates and integration examples. Distribute the repository link to all teams with instructions to reference it when implementing Bedrock features.

B : Implement pre-commit hooks in Git that run custom Python scripts to validate Bedrock API calls against a JSON schema defining allowed patterns before allowing commits.

C : Deploy SonarQube on Amazon EC2 to perform static code analysis. Configure custom rules that flag non-standard Bedrock integration patterns and generate reports for teams to manually fix.

D : Configure Amazon Q Developer to scan the codebase, identify Bedrock integration anti-patterns, generate refactored code following company standards, and apply the consistent changes across all microservice repositories.

Answer: D

Question 5

A media streaming company currently runs a self-managed vector database on Amazon EC2 instances for their content recommendation system. The database stores 150 million video embeddings and handles approximately 50,000 similarity searches per day during peak hours, with search activity dropping to near zero during off-peak nights.The infrastructure team spends significant time managing database scaling, performing backups, applying security patches, and monitoring cluster health. Monthly costs include $8,000 for always-on EC2 instances plus operational overhead. The CTO wants to reduce infrastructure management burden and optimize costs for the variable workload pattern.Which migration strategy addresses these requirements MOST cost-effectively?

Options :

A : Deploy Amazon OpenSearch Serverless with vector engine capabilities. Configure auto-scaling policies to scale compute capacity down to minimum during off-peak hours while maintaining search availability.

B : Containerize the existing vector database using Amazon ECS on AWS Fargate. Implement scheduled scaling policies to stop tasks during zero-activity periods and restart them before peak traffic begins.

C : Migrate the vector database to Amazon RDS for PostgreSQL with pgvector extension. Enable Aurora Serverless v2 auto-scaling to reduce costs during low-traffic periods while maintaining managed database benefits.

D : Migrate embeddings to Amazon S3 vector buckets with vector indexes enabled. Configure S3 Lifecycle policies for index management and leverage the pay-per-request pricing model that eliminates costs during zero-activity periods.

Answer: D

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