Question 1

A manufacturing company uses a legacy on-premises ERP system to track assembly line issues. The company wants to integrate Amazon Bedrock to automatically analyze "Technician Notes" (unstructured text) and categorize the root cause of failures using the Anthropic Claude 4.5 Sonnet model.The legacy ERP system has a strict limitation: it blocks all user operations while waiting for external API responses and times out if a response takes longer than 300 milliseconds. The GenAI analysis typically takes 3–5 seconds to complete.As a GenAI Developer, which integration architecture should you design to incorporate the Foundation Model (FM) capabilities without disrupting the legacy system's operations?

Options :

A : Use AWS AppSync with a GraphQL subscription. configure the ERP system to subscribe to the RootCauseAnalysis mutation. When a note is saved, trigger the mutation to invoke Amazon Bedrock directly. The subscription connection keeps the channel open without blocking the main ERP thread, allowing the response to stream back when ready.

B : Create an Amazon API Gateway REST API with a synchronous integration to an AWS Lambda function. Configure the ERP to call this API with the notes. Update the Lambda function to increase the memory allocation to 10 GB, ensuring the model inference completes within the 300-millisecond window.

C : Implement an asynchronous, event-driven architecture. Configure the ERP to push the technician notes to an Amazon SQS queue and immediately return success to the user. Use an AWS Lambda function to poll the queue, invoke the Amazon Bedrock model for analysis, and write the classification results back to the ERP database via a separate callback API.

D : Deploy an AWS PrivateLink connection between the on-premises network and the Amazon Bedrock service endpoint. Configure the ERP system to make direct HTTPS calls to the Bedrock InvokeModel API using the private endpoint, bypassing the public internet to reduce network latency below the 300-millisecond threshold.

Answer: C

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 large financial services enterprise is building a centralized platform to allow internal development teams to consume Foundation Models (FMs) from Amazon Bedrock. To ensure strict compliance and governance, the organization requires a "GenAI Gateway" architecture.The architecture must meet the following requirements:Prevent "Shadow AI" by forcing all internal applications to route requests through a single control point that logs every prompt and response for auditing.Automate the testing of prompt templates. Whenever a prompt engineer updates a standard system prompt, it must be automatically evaluated for toxicity and accuracy before being made available to applications.Ensure that PII is automatically redacted from prompts before they are sent to the model provider.Which combination of actions should you implement to achieve this?

Options :

A : Grant all internal application roles direct IAM permissions to bedrock:InvokeModel. Enable AWS CloudTrail data events for Amazon Bedrock to capture the request payloads and response bodies for auditing purposes.

B : Use Amazon Inspector within the CI/CD pipeline to scan the weights of the Amazon Bedrock base models for vulnerabilities and malware before approving them for use in the production environment.

C : Implement AWS AppSync with a direct data source to Amazon Bedrock. Use VTL resolvers to hardcode the system prompts within the API definition, ensuring that developers cannot modify them without redeploying the API.

D : Develop a centralized REST API using Amazon API Gateway and AWS Lambda. Configure the Lambda function to act as an abstraction layer that intercepts requests, uses Amazon Comprehend to detect and redact PII, logs the transaction to Amazon CloudWatch Logs, and then invokes Amazon Bedrock.

E : Create an AWS CodePipeline for prompt versioning. Include an AWS CodeBuild stage that runs an automated evaluation script (using a library like Ragas or specific Bedrock evaluations) against the new prompt candidate. Configure the pipeline to fail and rollback if the toxicity score exceeds a defined threshold.

Answer: D,E

Question 4

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 5

A financial analyst uses an internal GenAI tool to extract data from quarterly earnings PDFs into a standardized JSON format. The application is reporting frequent ValidationError exceptions in the backend. Upon investigation, the developer notices that for certain complex PDFs, the model either omits required fields or outputs the data as a Markdown table instead of JSON, despite the system prompt instructions.Which troubleshooting and remediation workflow should the developer implement to resolve these prompt maintenance issues and prevent recurrence?

Options :

A : Enable model invocation logging to Amazon CloudWatch Logs to capture the raw inputs and outputs. Use CloudWatch Logs Insights to filter for the failed requests and identify the specific document patterns causing the prompt confusion. Create a test suite of these edge cases, iterate on the prompt template by adding "few-shot" examples of the correct JSON structure, and implement strict schema validation to detect format inconsistencies before processing.

B : Configure AWS X-Ray to trace the latency of the model invocations. Identify the requests that take longer than 5 seconds to generate a response, as these indicate prompt confusion. Switch the Foundation Model to a lower-latency version (e.g., from Claude to Titan) to ensure the model responds before the application times out.

C : Use Amazon SageMaker Model Monitor to track the statistical bias of the generated JSON output. Configure a baseline for the expected distribution of keys in the JSON object. Set up an alert to trigger if the drift in key frequency exceeds 10%, indicating a format inconsistency.

D : Implement an Amazon EventBridge rule to trigger an automatic retry with a higher temperature setting (e.g., 1.0) whenever the backend reports a ValidationError. This encourages the model to generate a different, more creative output format that might pass validation on the second attempt.

Answer: A

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