AIP-C01 practice questions 91–100

1. Question 91 · 100 in bank

   A company is using Amazon Bedrock to develop a customer support AI assistant. The AI assistant must respond to customer questions about their accounts. The AI assistant must not expose personal information in responses. The company must comply with data residency policies by ensuring that all processing occurs within the same AWS Region where each customer is located. The company wants to evaluate how effective the AI assistant is at preventing the exposure of personal information before the company makes the AI assistant available to customers. Which solution will meet these requirements?

   • AConfigure a cross-Region Amazon Bedrock guardrail to apply sensitive information filters. Set the guardrail to detect mode during development and testing. Switch to block mode for production deployment.
   • BConfigure an Amazon Bedrock guardrail to apply sensitive information filters. Set the guardrail to mask mode during development and testing. Switch to block mode for production deployment. Deploy a copy of the guardrail to each Region where the company operates.
   • CConfigure an Amazon Bedrock guardrail to apply content and topic filters. Set the guardrail to detect mode during development, testing, and production. Disable invocation logging for the Amazon Bedrock model.
   • DConfigure a cross-Region Amazon Bedrock guardrail to apply a set of content and word filters. Set the guardrail to detect mode during development and testing. Switch to mask mode for production deployment.

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   Answer: B

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2. Question 92 · 100 in bank

   A university recently digitized a collection of archival documents, academic journals, and manuscripts. The university stores the digital files in an AWS Lake Formation data lake. The university hires a GenAI developer to build a solution to allow users to search the digital files by using text queries. The solution must return journal abstracts that are semantically similar to a user's query. Users must be able to search the digitized collection based on text and metadata that is associated with the journal abstracts. The metadata of the digitized files does not contain keywords. The solution must match similar abstracts to one another based on the similarity of their text. The data lake contains fewer than 1 million files. Which solution will meet these requirements with the LEAST operational overhead?

   • AUse Amazon Titan Embeddings in Amazon Bedrock to create vector representations of the digitized files. Store embeddings in the OpenSearch Neural Plugin for Amazon OpenSearch Service.
   • BUse Amazon Comprehend to extract topics from the digitized files. Store the topics and file metadata in an Amazon Aurora PostgreSQL database. Query the abstract metadata against the data in the Aurora database.
   • CUse Amazon SageMaker AI to deploy a sentence-transformer model. Use the model to create vector representations of the digitized files. Store embeddings in an Amazon Aurora PostgreSQL database that has the pgvector extension.
   • DUse Amazon Titan Embeddings in Amazon Bedrock to create vector representations of the digitized files. Store embeddings in an Amazon Aurora PostgreSQL Serverless database that has the pgvector extension.

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   Answer: D

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3. Question 93 · 100 in bank

   Select 2

   A financial services company wants to use Amazon Bedrock foundation models (FMs) to analyze call center recordings. When calls end, the call center stores recordings as MP3 files in an Amazon S3 bucket. The company needs to generate summaries and sentiment analysis for the recordings in a structured format as soon as new files are created. The recordings average 20 MB in size. Which combination of solutions will meet these requirements? (Choose two.)

   • AUse AWS Step Functions to orchestrate a workflow to process the recordings. Configure steps to invoke Amazon Transcribe to convert audio to text, validate job completion, and to invoke an AWS Lambda function to process the text by using Amazon Bedrock FMs to generate structured analysis output.
   • BUse AWS Step Functions to orchestrate a workflow to process the recordings. Configure steps to invoke Amazon Transcribe to convert audio to text, validate job completion, and to directly invoke Amazon Bedrock FMs to generate summaries and sentiment analysis in JSON format.
   • CUse AWS Step Functions to orchestrate a workflow to process the recordings. Configure steps to invoke Amazon Transcribe to convert audio to text, validate job completion, and to invoke an AWS Lambda function to create a prompt to invoke Amazon Bedrock FMs to generate structured analysis output.
   • DConfigure the source S3 bucket to send events to Amazon EventBridge. Create an EventBridge rule to invoke the Step Functions workflow when an object is created in the bucket.
   • EConfigure the source S3 bucket to send notifications to the Step Functions workflow when an object is created in the bucket.

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   Answer: CD

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4. Question 94 · 100 in bank

   A medical device company wants to feed reports of medical procedures that used the company's devices into an AI assistant. To protect patient privacy, the AI assistant must expose patient personally identifiable information (PII) only to surgeons. The AI assistant must redact PII for engineers. The AI assistant must reference only medical reports that are less than 3 years old. The company stores reports in an Amazon S3 bucket as soon as each report is published. The company has already set up an Amazon Bedrock knowledge base. The AI assistant uses Amazon Cognito to authenticate users. Which solution will meet these requirements?

   • AEnable Amazon Macie PII detection on the S3 bucket. Use an S3 trigger to invoke an AWS Lambda function that redacts PII from the reports. Configure the Lambda function to delete outdated documents from the bucket and to invoke knowledge base syncing.
   • BInvoke an AWS Lambda function to sync the S3 bucket and the knowledge base when a new report is uploaded to the bucket. Use a second Lambda function to invoke Amazon Comprehend to detect and redact PII if a user is part of the engineer Cognito user group. Set up an S3 Lifecycle configuration to remove reports that are older than 3 years from the bucket.
   • CSet up an S3 Lifecycle configuration on the bucket to remove reports that are older than 3 years. Schedule an AWS Lambda function to run daily syncs between the bucket and the knowledge base. When users interact with the AI assistant, call the ApplyGuardrail configuration that matches the user's Cognito user group to redact PII from the agent’s responses if appropriate.
   • DCreate a second knowledge base. Set up an S3 Lifecycle configuration on the bucket to remove reports that are older than 3 years. Invoke an AWS Lambda function that syncs the bucket with the original knowledge base when a new report is uploaded to the bucket. Use Amazon Comprehend to detect and redact PII before syncing the bucket with the second knowledge base. When a user interacts with the AI assistant, redirect the model to the appropriate knowledge base depending on the user’s Cognito user group.

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   Answer: C

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5. Question 95 · 100 in bank

   A large ecommerce company has deployed a foundation model (FM) to generate product descriptions. The company's engineering team monitors technical metrics such as token usage, latency, and error rates by using Amazon CloudWatch. The company's marketing team tracks business metrics such as conversion rates and revenue impact in its own systems. The company needs a unified observability solution that correlates technical performance with business outcomes. The solution must provide automatic alerts to stakeholders when operational metrics indicate degradation. The solution must provide comprehensive visibility across both technical and business metrics. Which solution will meet these requirements?

   • ACreate CloudWatch dashboards that include technical metrics and imported business metrics. Configure CloudWatch composite alarms that combine technical data and business data. Use Amazon SNS to set up notifications to stakeholders.
   • BUse Amazon Managed Grafana to visualize technical metrics from CloudWatch with business metrics from external sources. Configure Amazon Managed Grafana alerts to invoke AWS Lambda functions. Configure the Lambda functions to remediate issues automatically when metrics exceed predefined thresholds.
   • CStream CloudWatch metrics to Amazon S3 by using CloudWatch metric streams. Create Amazon QuickSight dashboards to visualize the combined technical metrics and business metrics. Set up Amazon EventBridge rules to send notifications to stakeholders when metrics exceed predefined thresholds.
   • DConfigure CloudWatch custom dashboards that integrate operational metrics with imported business metrics. Set up CloudWatch composite alarms with anomaly detection. Use Amazon SNS to create alarm actions to notify stakeholders when correlated metrics indicate performance issues.

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   Answer: D

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6. Question 96 · 100 in bank

   A GenAI developer is evaluating Amazon Bedrock foundation models (FMs) to enhance a Europe-based company's internal business application. The company has a multi-account landing zone in AWS Control Tower. The company uses SCPs to allow its accounts to use only the eu-north-1 Region and the eu-west-1 Region. All customer data must remain in private networks within the approved AWS Regions. The GenAI developer selects an FM based on analysis and testing and hosts the model in the eu-central-1 Region and the eu-west-3 Region. The GenAI developer must enable access to the FM for the company’s employees. The GenAI developer must ensure that requests to the FM are private and remain with the same Regions as the FM. Which solution will meet these requirements?

   • ADeploy an AWS Lambda function that is exposed by a private Amazon API Gateway REST API to a VPC in eu-north-1. Create a VPC endpoint for the selected FM in eu-central-1 and eu-west-3. Extend existing SCPs to allow employees to use the FM. Integrate the REST API with the business application.
   • BDeploy the FM on Amazon EC2 instances in eu-north-1. Deploy a private Amazon API Gateway REST API in front of the EC2 instances. Configure an Amazon Bedrock VPC endpoint. Integrate the REST API with the business application.
   • CConfigure the FM to use cross-Region inference through an eu.amazon.* endpoint to ensure that all calls remain within Europe. Configure an Amazon Bedrock VPC endpoint. Extend existing SCPs to allow employees to use the FM through inference profiles in Europe-based Regions where the FM is available. Use an inference profile to integrate Amazon Bedrock with the business application.
   • DDeploy the FM in Amazon SageMaker AI in eu-north-1. Configure a SageMaker AI VPC endpoint. Extend existing SCPs to allow employees to use the SageMaker AI endpoint. Integrate the FM in SageMaker AI with the business application.

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   Answer: C

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7. Question 97 · 100 in bank

   A healthcare company is developing an application to process medical queries. The application must answer complex queries with high accuracy by reducing semantic dilution. The application must refer to domain-specific terminology in medical documents to reduce ambiguity in medical terminology. The application must be able to respond to 1,000 queries each minute with response times less than 2 seconds. Which solution will meet these requirements with the LEAST operational overhead?

   • AUse Amazon API Gateway to route incoming queries to an Amazon Bedrock agent. Configure the agent to use an Anthropic Claude model to decompose queries and an Amazon Titan model to expand queries. Create an Amazon Bedrock knowledge base to store the reference medical documents.
   • BConfigure an Amazon Bedrock knowledge base to store the reference medical documents. Enable query decomposition in the knowledge base. Configure an Amazon Bedrock flow that uses a foundation model (FM) and the knowledge base to support the application.
   • CUse Amazon SageMaker Al to host custom ML models for both query decomposition and query expansion. Configure Amazon Bedrock knowledge bases to store the reference medical documents. Encrypt the documents in the knowledge base.
   • DCreate an Amazon Bedrock agent to orchestrate multiple AWS Lambda functions to decompose queries. Create an Amazon Bedrock knowledge base to store the reference medical documents. Use the agent's built-in knowledge base capabilities. Add deep research and reasoning capabilities to the agent to reduce ambiguity in the medical terminology.

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   Answer: B

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8. Question 98 · 100 in bank

   A company is creating a generative AI (GenAI) application that uses Amazon Bedrock foundation models (FMs). The application must use Microsoft Entra ID to authenticate. All FM API calls must stay on private network paths. Access to the application must be limited by department to specific model families. The company also needs a comprehensive audit trail of model interactions. Which solution will meet these requirements?

   • AConfigure SAML federation between Microsoft Entra ID and IAM. Create department-specific IAM roles that allow only the required ModelId values. Create AWS PrivateLink interface VPC endpoints for Amazon Bedrock runtime services. Enable AWS CloudTrail to capture Amazon Bedrock API calls. Configure Amazon Bedrock model invocation logging to record detailed model interactions.
   • BCreate an identity provider (IdP) connection in IAM to authenticate by using Microsoft Entra ID. Assign department permission sets to control access to specific model families. Deploy AWS Lambda functions in private subnets with a NAT gateway for egress to Amazon Bedrock public endpoints. Enable CloudWatch Logs to capture model interactions for auditing purposes.
   • CCreate a SAML identity provider (IdP) in IAM to authenticate by using Microsoft Entra ID. Use IAM permissions boundaries to limit department roles’ access to specific model families. Configure public Amazon Bedrock API endpoints with VPC routing to maintain private network connectivity. Set up AWS CloudTrail with Amazon S3 Lifecycle rules to manage audit logs of model interactions.
   • DConfigure OpenID Connect (OIDC) federation between Microsoft Entra ID and IAM. Use attribute-based access control to map department attributes to specific model access permissions. Apply SCP policies to restrict access to Amazon Bedrock FM families based on department. Use Microsoft Entra ID’s built-in logging capabilities to maintain an audit trail of model interactions.

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   Answer: A

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9. Question 99 · 100 in bank

   A hospital is building an AI application to help medical clinicians to make treatment decisions. The application uses Amazon Bedrock to analyze patient case histories and suggest diagnoses. The application must maintain sub-500 ms response times to integrate with the hospital’s existing real-time clinical workflow. To comply with privacy regulations, the application must log all per-sonally identifiable information (PII) handling decisions for audits. The application must detect and remove PII from responses with at least 99% accuracy. After initial deployment, clinicians report that diagnostic summaries from the application occa-sionally include patient names and medical record numbers that were not present in the original case history inputs. An investigation reveals that Amazon Comprehend Medical successfully de-tects and removes PII from inputs with 95% accuracy, and the application replaces all detected entities with tokens before it sends inputs to Amazon Bedrock. However, the application contin-ues to generate patient-identifying information in approximately 3-5% of outputs. The company needs a solution to prevent the application from displaying PII in outputs while meeting all other operational requirements. Which solution will meet these requirements?

   • AConfigure Amazon Bedrock guardrails with sensitive information filters to detect and block PII in model outputs.
   • BImplement a secondary PII detection layer by using regular expressions and custom entity recognition to detect identifiers that Amazon Comprehend Medical misses before sending inputs to Amazon Bedrock.
   • CRemove detailed medical context from case histories during pre-processing to prevent the model from generating patient-specific information based on clinical pattern associations.
   • DEnable session isolation in Amazon Bedrock API calls. Clear conversation history between requests to prevent patient information from persisting across multiple case analyses.

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   Answer: A

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10. Question 100 · 100 in bank

    A financial services company is building a fraud detection system by using Amazon Bedrock. The system will monitor activity in multiple stock trading applications that run in the United States and Europe. The system must process 1,000 transactions every second with sub-500 ms response times. The system must also maintain high availability during connectivity disruptions. The company must ensure that data for European customers is processed only in AWS Regions that are based in Europe. Which solution will meet these requirements?

    • AConfigure AWS Lambda functions and Amazon EKS applications to use the InvokeModel API with a global inference profile. Deploy an automated failover system that uses Amazon Route 53 health checks. Create a dedicated European inference profile and enable Geographic cross-Region inference for European applications. Use Amazon CloudWatch alarms to monitor utilization metrics.
    • BConfigure all applications to use the InvokeModel API with provisioned throughput for an Anthropic Claude model in each Region separately. Set up a custom Application Load Balancer to distribute traffic based on Regional capacity and response times. Implement a Regional failover mechanism that uses Amazon EventBridge rules to handle connectivity disruptions.
    • CConfigure all applications to use the InvokeModelWithResponseStream API with on-demand throughput. Deploy an Amazon API Gateway REST API with Regional endpoints in each location where the company operates to route requests to the closest Amazon Bedrock endpoint. Create separate IAM roles for applications that run in the United States and Europe. Grant the IAM roles Region-specific permissions.
    • DConfigure applications that run in the United States to use provisioned throughput with the InvokeModel API. Configure European applications to use a Europe-specific geographic inference profile to ensure data sovereignty. Configure automatic scaling for provisioned capacity based on utilization metrics. Use Amazon EventBridge and AWS Lambda functions to implement cross-Region failover mechanisms.

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    Answer: A

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AIP-C01 practice, question 91. 100 questions available.