AIP-C01 practice questions 81–90

1. Question 81 · 100 in bank

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   A company uses an organization in AWS Organizations with all features enabled to manage multiple AWS accounts. Employees use Amazon Bedrock across multiple accounts. The company must prevent specific topics and proprietary information from being included in prompts to Amazon Bedrock models. The company must ensure that employees can use only approved Amazon Bedrock models. The company centrally manages IAM roles for employees. Which combination of solutions will meet these requirements? (Choose two.)

   • ACreate an IAM permissions boundary for each employee's IAM role. Configure the permissions boundary to require an approved Amazon Bedrock guardrail identifier to invoke Amazon Bedrock models. Create an SCP that allows employees to use only approved models.
   • BCreate an SCP that allows employees to use only approved models. Configure the SCP to require employees to specify a guardrail identifier in calls to invoke an approved model.
   • CCreate an SCP that prevents an employee from invoking a model if a centrally deployed guardrail identifier is not specified in a call to the model. Create a permissions boundary on each employee's IAM role that allows each employee to invoke only approved models.
   • DUse AWS CloudFormation to create a custom Amazon Bedrock guardrail that has a block filtering policy. Use stack sets to deploy the guardrail to each account in the organization.
   • EUse AWS CloudFormation to create a custom Amazon Bedrock guardrail that has a mask filtering policy. Use stack sets to deploy the guardrail to each account in the organization.

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   Answer: BD

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2. Question 82 · 100 in bank

   A company is designing an API for a generative AI (GenAI) application that uses a foundation model (FM) that is hosted on a managed model service. The API must stream responses to reduce latency, enforce token limits to manage compute resource usage, and implement retry logic to handle model timeouts and partial responses. Which solution will meet these requirements with the LEAST operational overhead?

   • AIntegrate an Amazon API Gateway HTTP API with an AWS Lambda function to invoke Amazon Bedrock. Use Lambda response streaming to stream responses. Enforce token limits within the Lambda function. Implement retry logic for model timeouts by using Lambda and API Gateway timeout configurations.
   • BConnect an Amazon API Gateway HTTP API directly to Amazon Bedrock. Simulate streaming by using client-side polling. Enforce token limits on the frontend. Configure retry behavior by using API Gateway integration settings.
   • CConnect an Amazon API Gateway WebSocket API to an Amazon ECS service that hosts a containerized inference server. Stream responses by using the WebSocket protocol. Enforce token limits within Amazon ECS. Handle model timeouts by using ECS task lifecycle hooks and restart policies.
   • DIntegrate an Amazon API Gateway REST API with an AWS Lambda function that invokes Amazon Bedrock. Use Lambda response streaming to stream responses. Enforce token limits within the Lambda function. Implement retry logic by using Lambda and API Gateway timeout configurations.

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   Answer: D

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3. Question 83 · 100 in bank

   A retail company is using Amazon Bedrock to develop a customer service AI assistant. Analysis shows that 70% of customer inquiries are simple product questions that a smaller model can effectively handle. However, 30% of inquiries are complex return policy questions that require advanced reasoning. The company wants to implement a cost-effective model selection framework to automatically route customer inquiries to appropriate models based on inquiry complexity. The framework must maintain high customer satisfaction and minimize response latency. Which solution will meet these requirements with the LEAST implementation effort?

   • ACreate a multi-stage architecture that uses a small foundation model (FM) to classify the complexity of each inquiry. Route simple inquiries to a smaller, more cost-effective model. Route complex inquiries to a larger, more capable model. Use AWS Lambda functions to handle the routing logic.
   • BUse Amazon Bedrock intelligent prompt routing to automatically analyze inquiries. Route simple product inquiries to smaller models, and route complex return policy inquiries to more capable larger models.
   • CImplement a single-model solution that uses an Amazon Bedrock mid-sized foundation model (FM) with on-demand pricing. Include special instructions in model prompts to handle both simple and complex inquiries by using the same model.
   • DCreate separate Amazon Bedrock endpoints for simple and complex inquiries. Implement a rule-based routing system based on keyword detection. Use on-demand pricing for the smaller model and provisioned throughput for the larger model.

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   Answer: B

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4. Question 84 · 100 in bank

   A specialty coffee company has a mobile app that generates personalized coffee roast profiles by using Amazon Bedrock with a three-stage prompt chain. The prompt chain converts user inputs into structured metadata, retrieves relevant logs for coffee roasts, and generates a personalized roast recommendation for each customer. Users in multiple AWS Regions report inconsistent roast recommendations for identical inputs, slow inference during the retrieval step, and unsafe recommendations such as brewing at excessively high temperatures. The company must improve the stability of outputs for repeated inputs. The company must also improve app performance and the safety of the app's outputs. The updated solution must ensure 99.5% output consistency for identical inputs and achieve inference latency of less than 1 second. The solution must also block unsafe or hallucinated recommendations by using validated safety controls. Which solution will meet these requirements?

   • ADeploy Amazon Bedrock with provisioned throughput to stabilize inference latency. Apply Amazon Bedrock guardrails that have semantic denial rules to block unsafe outputs. Use Amazon Bedrock Prompt Management to manage prompts by using approval workflows.
   • BUse Amazon Bedrock Agents to manage chaining. Log model inputs and outputs to Amazon CloudWatch Logs. Use logs from Amazon CloudWatch to perform A/B testing for prompt versions.
   • CCache prompt results in Amazon ElastiCache. Use AWS Lambda functions to pre-process metadata and to trace end-to-end latency. Use AWS X-Ray to identify and remediate performance bottlenecks.
   • DUse Amazon Kendra to improve roast log retrieval accuracy. Store normalized prompt metadata within Amazon DynamoDB. Use AWS Step Functions to orchestrate multistep prompts.

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   Answer: A

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5. Question 85 · 100 in bank

   A company is developing a generative AI (GenAI) application by using Amazon Bedrock. The application will analyze patterns and relationships in the company's data. The application will process millions of new data points daily across AWS Regions in Europe, North America, and Asia before storing the data in Amazon S3. The application must comply with local data protection and storage regulations. Data residency and processing must occur within the same continent. The application must also maintain audit trails of the application's decision-making processes and provide data classification capabilities. Which solution will meet these requirements?

   • ADeploy the application in each Region with local IAM policies. Use Amazon Bedrock cross-Region inference to distribute the workload. Use Amazon CloudWatch to log AI decision-making processes and data processing activities. Manually track compliance certifications across Regions.
   • BUse SCPs with AWS Organizations to manage location-specific permissions. Use AWS CloudTrail immutable logs to audit the decision-making processes. Import a custom model into Amazon Bedrock and deploy the model to each Region.
   • CUse Amazon S3 Object Lock with Region-specific S3 bucket policies. Pre-process the data points within the Region based on geographic origin before sending the data points to Amazon Bedrock. Use Amazon Macie to classify the data. Use AWS CloudTrail immutable logs to audit the decision-making processes.
   • DCreate separate AWS accounts for each Region with individual compliance frameworks. Use Amazon SageMaker AI with custom monitoring to track model performance and compliance with data residency requirements. Create manual reports for each regulatory jurisdiction.

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   Answer: C

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6. Question 86 · 100 in bank

   A financial services company is deploying a generative AI (GenAI) application that uses Amazon Bedrock to assist customer service representatives to provide personalized investment advice to customers. The company must implement a comprehensive governance solution that follows responsible AI practices and meets regulatory requirements. The solution must detect and prevent hallucinations in recommendations. The solution must have safety controls for customer interactions. The solution must also monitor model behavior drift in real time and maintain audit trails of all prompt-response pairs for regulatory review. The company must deploy the solution within 60 days. The solution must integrate with the company's existing compliance dashboard and respond to customers within 200 ms. Which solution will meet these requirements with the LEAST operational overhead?

   • AConfigure Amazon Bedrock guardrails to apply custom content filters and toxicity detection. Use Amazon Bedrock Model Evaluation to detect hallucinations. Store prompt-response pairs in Amazon DynamoDB to capture audit trails and set a TTL. Integrate Amazon CloudWatch custom metrics with the existing compliance dashboard.
   • BDeploy Amazon Bedrock and use AWS PrivateLink to access the application securely. Use AWS Lambda functions to implement custom prompt validation. Store prompt-response pairs in an Amazon S3 bucket and configure S3 Lifecycle policies. Create custom Amazon CloudWatch dashboards to monitor model performance metrics.
   • CUse Amazon Bedrock Agents and Amazon Bedrock Knowledge Bases to ground responses. Use Amazon Bedrock Guardrails to enforce content safety. Use Amazon OpenSearch Service to store and index prompt-responses pairs. Integrate OpenSearch Service with Amazon QuickSight to create compliance reports and to detect model behavior drift.
   • DUse Amazon SageMaker Model Monitor to detect model behavior drift. Use AWS WAF to filter content. Store customer interactions in an encrypted Amazon RDS database. Use Amazon API Gateway to create custom HTTP APIs to integrate with the compliance dashboard.

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   Answer: A

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7. Question 87 · 100 in bank

   A company uses AWS Lake Formation to set up a data lake that contains databases and tables for multiple business units across multiple AWS Regions. The company wants to use a foundation model (FM) through Amazon Bedrock to perform fraud detection. The FM must ingest sensitive financial data from the data lake. The data includes some customer personally identifiable information (PM). The company must design an access control solution that prevents PI I from appearing in a production environment. The FM must access only authorized data subsets that have PH redacted from specific data columns. The company must capture audit trails for all data access. Which solution will meet these requirements?

   • ACreate a separate dataset in a separate Amazon S3 bucket for each business unit and Region combination. Configure S3 bucket policies to control access based on IAM roles that are assigned to FM training instances. Use S3 access logs to track data access.
   • BConfigure the FM to authenticate by using IAM roles and Lake Formation permissions based on LF-Tag expressions. Define business units and Regions as LF-Tags that are assigned to databases and tables. Use AWS CloudTrail to collect comprehensive audit trails of data access.
   • CUse direct IAM principal grants on specific databases and tables in Lake Formation. Create a custom application layer that logs access requests and further filters sensitive columns before sending data to the FM.
   • DConfigure the FM to request temporary credentials from AWS STS. Access the data by using presigned S3 URLs that are generated by an API that applies business unit and Regional filters. Use AWS CloudTrail to collect comprehensive audit trails of data access.

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   Answer: B

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8. Question 88 · 100 in bank

   A company runs a Retrieval Augmented Generation (RAG) application that uses Amazon Bedrock Knowledge Bases to perform regulatory compliance queries. The application uses the RetrieveAndGenerateStream API. The application retrieves relevant documents from a knowledge base that contains more than 50,000 regulatory documents, legal precedents, and policy updates. The RAG application is producing suboptimal responses because the initial retrieval often returns semantically similar but contextually irrelevant documents. The poor responses are causing model hallucinations and incorrect regulatory guidance. The company needs to improve the performance of the RAG application so it returns more relevant documents. Which solution will meet this requirement with the LEAST operational overhead?

   • ADeploy an Amazon SageMaker endpoint to run a fine-tuned ranking model. Use an Amazon API Gateway REST API to route requests. Configure the application to make requests through the REST API to rerank the results.
   • BUse Amazon Comprehend to classify documents and apply relevance scores. Integrate the RAG application’s reranking process with Amazon Textract to run document analysis. Use Amazon Neptune to perform graph-based relevance calculations.
   • CImplement a retrieval pipeline that uses the Amazon Bedrock Knowledge Bases Retrieve API to perform initial document retrieval. Call the Amazon Bedrock Rerank API to rerank the results. Invoke the InvokeModelWithResponseStream operation to generate responses.
   • DUse the latest Amazon reranker model through the reranking configuration within Amazon Bedrock Knowledge Bases. Use the model to improve document relevance scoring and to reorder results based on contextual assessments.

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   Answer: D

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9. Question 89 · 100 in bank

   A company is developing a customer communication platform that uses an AI assistant powered by an Amazon Bedrock foundation model (FM). The AI assistant summarizes customer messages and generates initial response drafts. The company wants to use Amazon Comprehend to implement layered content filtering. The layered content filtering must prevent sharing of offensive content, protect customer privacy, and detect potential inappropriate advice solicitation. Inappropriate advice solicitation includes requests for unethical practices, harmful activities, or manipulative behaviors. The solution must maintain acceptable overall response times, so all pre-processing filters must finish before the content reaches the FM. Which solution will meet these requirements?

   • AUse parallel processing with asynchronous API calls. Use toxicity detection for offensive content. Use prompt safety classification for inappropriate advice solicitation. Use personally identifiable information (PII) detection without redaction.
   • BUse custom classification to build an FM that detects offensive content and inappropriate advice solicitation. Apply personally identifiable information (PII) detection as a secondary filter only when messages pass the custom classifier.
   • CDeploy a multi-stage process. Configure the process to use prompt safety classification first, then toxicity detection on safe prompts only, and finally personally identifiable information (PII) detection in streaming mode. Route flagged messages through Amazon EventBridge for human review.
   • DUse toxicity detection with thresholds configured to 0.5 for all categories. Use parallel processing for both prompt safety classification and personally identifiable information (PII) detection with entity redaction. Apply Amazon CloudWatch alarms to filter metrics.

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   Answer: D

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10. Question 90 · 100 in bank

    A software as a service (SaaS) company is building a recommendation model that uses Amazon SageMaker AI to support an application that recommends airline cabin upgrades to customers. The company will host SageMaker AI models on Amazon Bedrock by using Amazon Bedrock Custom Model Import. Airline companies will use the application to send customized offers to customers. The model must examine the travel history of customers to help make more relevant recommendations. The company stores customer travel history data in an Amazon RDS database. The company must ensure that the application delivers consistent, relevant, and accurate results across multiple airlines and customer populations. Which solution will meet these requirements?

    • AUse Amazon Bedrock Knowledge Bases to implement a RAG architecture to analyze customer travel history data to give the application semantic search capabilities. Use the semantic search capabilities to retrieve relevant booking patterns, preferences, and loyalty information to generate personalized cabin upgrade recommendations. Apply Amazon Bedrock guardrails to filter content. Use AWS Step Functions and AWS Lambda functions to orchestrate validation workflows to reduce hallucinations.
    • BImplement text-to-SQL transformations with SQL validations to accurately retrieve relevant booking patterns, preferences, and loyalty information from the RDS database. Use the results to generate personalized cabin upgrade recommendations. Apply Amazon Bedrock guardrails to filter content. Use AWS Step Functions and AWS Lambda functions to orchestrate validation workflows to reduce hallucinations.
    • CUse Amazon OpenSearch Service to implement vector searches of customer travel history embeddings. Use the vector searches to give the application the ability to perform similarity-based retrieval of booking patterns, preferences, and loyalty information to generate personalized cabin upgrade recommendations. Apply Amazon Bedrock guardrails to filter responses. Use confidence scoring and semantic similarity searches to reduce hallucinations.
    • DImplement text-to-SQL transformations with SQL validations to accurately retrieve relevant booking patterns, preferences, and loyalty information from the RDS database. Use the results to generate personalized cabin upgrade recommendations. Apply Amazon Bedrock guardrails to filter responses. Use confidence scoring and semantic similarity searches to reduce hallucinations.

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    Answer: B

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AIP-C01 practice, question 81. 100 questions available.