Question 1

An insurance company has deployed a Generative AI chatbot using Amazon Bedrock to explain policy details to customers. To comply with strict financial regulations, the solution requires a continuous monitoring and automated remediation system for two specific governance issues:Misuse Remediation: If a specific user triggers the "Hate Speech" content filter in Amazon Bedrock Guardrails more than 5 times in a single minute, their access must be automatically and immediately suspended.Bias Monitoring: The system must strictly monitor the model's responses over time to detect if the terminology becomes statistically biased against specific demographics compared to a pre-established "Golden Set" baseline.Which combination of mechanisms satisfies these requirements?

Options :

A : Implement a scheduled AWS Step Functions workflow that uses Amazon SageMaker Clarify to analyze the aggregated inference logs stored in Amazon S3. Configure the job to calculate bias metrics against the baseline and publish findings to CloudWatch.

B : Deploy Amazon SageMaker Model Monitor on the Amazon Bedrock endpoint. Enable the "Real-time Concept Drift" feature to block any inference response that deviates more than 5% from the training data distribution.

C : Configure an Amazon EventBridge Rule to match the aws.bedrock.GuardrailIntervention event pattern. Set the target as an AWS Systems Manager Run Command to disable the user's account in the identity provider.

D : Enable Amazon Macie on the bucket containing the inference logs. configure a custom data identifier for "Biased Terminology" and set up an automated remediation job to delete non-compliant logs.

E : Create an Amazon CloudWatch Alarm that monitors the GuardrailInterventionCount metric with a distinct dimension for the user ID. Configure the alarm action to trigger an AWS Lambda function that revokes the user's IAM permissions or API keys.

Answer: A,E

Question 2

A content moderation system needs to identify specific grammatical patterns in user-generated text, such as detecting imperative sentences (commands) or questions. The team wants to understand the grammatical structure and parts of speech (nouns, verbs, adjectives) in text before making moderation decisions.Which Amazon Comprehend feature should they use?

Options :

A : Sentiment analysis

B : Syntax analysis (part-of-speech tagging)

C : Entity recognition

D : Key phrase extraction

Answer: B

Question 3

A bank uses an AWS CodePipeline to deploy a GenAI loan approval application. The application consists of an Amazon Bedrock Knowledge Base and a set of Lambda functions. The compliance team requires that before any new Knowledge Base configuration (e.g., chunking strategy change) is promoted to Production, it must pass a "Golden Set" evaluation.If the evaluation score (F1 score) is below 0.85, the pipeline must automatically rollback.Which solution meets these requirements?

Options :

A : Use Amazon Inspector within the pipeline to scan the Knowledge Base configuration for misconfigurations and vulnerabilities.

B : Add a Manual Approval action to the pipeline. Configure an Amazon SNS notification to the compliance team with a link to the Bedrock Console so they can manually test the agent and click "Approve."

C : Add a Test Stage to the pipeline. Use AWS CodeBuild to run a script that invokes the new Knowledge Base Retrieve API against the golden questions. Calculate the score and return a non-zero exit code if it fails, causing the pipeline to stop.

D : Use AWS CloudFormation Guard. Define a policy that checks the VectorIndexConfiguration parameter in the template. Fail the stack deployment if the chunking strategy has changed.

Answer: C

Question 4

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 5

A travel company is building a complex "Vacation Planner" system using Amazon Bedrock Agents multi-agent collaboration:Flight Specialist Agent: Searches flight databases using a Lambda-based action groupHotel Specialist Agent: Searches hotel inventory using an API-based action groupSupervisor Agent: Interacts with the user, breaks down the "Plan a trip to Paris" request, delegates sub-tasks to the specialist agents, and aggregates their findings into a final itineraryThe developer needs to optimize the system to reduce latency for simple, single-domain queries (like "Find me a hotel in Paris") while maintaining the ability to handle complex multi-step requests (like "Plan a complete 5-day vacation to Paris").Which collaboration mode configuration meets this requirement?

Options :

A : Configure the supervisor agent in Supervisor with routing mode. Simple queries are routed directly to the appropriate collaborator agent (Flight or Hotel Specialist), while complex queries automatically trigger full supervisor mode for orchestration across multiple agents.

B : Configure the supervisor agent in Supervisor mode. The supervisor always breaks down the request, invokes collaborator agents in parallel, and synthesizes the results regardless of query complexity.

C : Deploy all three agents as independent agents without multi-agent collaboration. Use Amazon Lex to parse user intent and route to the appropriate single agent, then use a Lambda function to aggregate results from multiple agent invocations.

D : Use AWS Step Functions to create a Choice state that evaluates query complexity. Route simple queries to individual agents via direct Lambda invocation, and complex queries to the Bedrock supervisor agent.

Answer: A

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