Question 1

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 2

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 3

A marketing analytics company generates monthly reports that include AI-generated summaries of customer feedback. They have 10,000 customer feedback entries to summarize each month. The summaries are not time-sensitive and can be processed overnight. The team wants to optimize costs compared to processing requests one-by-one in real-time.Which processing approach should the developer use to reduce costs?

Options :

A : Invoke the foundation model in real-time for each customer feedback entry as soon as it arrives

B : Use provisioned throughput to guarantee capacity for all 10,000 requests

C : Use batch processing to send multiple requests together and process them during off-peak hours

D : Enable auto-scaling to handle the large burst of requests

Answer: C

Question 4

A news aggregator uses a RAG architecture with Amazon OpenSearch Service (Provisioned) as the vector store. The system ingests breaking news in real-time, performing thousands of vector insertions per minute.Users are reporting that the "Related Articles" search is becoming increasingly slow (high latency) during these ingestion spikes. The OpenSearch cluster metrics show high CPU utilization and high JVM memory pressure.The index is configured with the standard settings for k-NN search.Which index configuration change should the developer apply to improve search performance under heavy ingestion load?

Options :

A : Change the vector data type from float32 to binary to reduce the storage footprint.

B : Switch the index engine from nmslib to faiss and enable the ivf (Inverted File) algorithm.

C : Enable the "Force Merge" operation to run automatically every minute to reduce the number of segments in the index.

D : Increase the refresh_interval setting of the OpenSearch index (e.g., from 1s to 30s or 60s).

Answer: D

Question 5

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

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