Amazon AIP-C01 Question Answers

Question # 11

A company is developing three specialized NLP models that support a customer service application. One model categorizes each customer’s specific issue. Another model extracts key information from the customer interactions. The third model generates responses. The company must ensure that the application achieves at least 95% accuracy for all tasks. The application must handle up to 500 concurrent requests and respond in less than 500 ms during daily 2-hour peak usage periods. The company must ensure that the application optimizes resource usage during periods of low demand between usage spikes. Which solution will meet these requirements?

A. Deploy all three models to a single Amazon SageMaker AI multi-model endpoint. Enable dynamic scaling on the endpoint. Use a compute optimized instance type. Configure auto scaling policies that are based on invocation metrics to handle peak loads. 
B. Deploy each model to a separate Amazon SageMaker Serverless Inference endpoint. Set provisioned concurrency to handle peak loads. Configure maximum concurrency limits and memory sizing based on each model's specific requirements. 
C. Deploy the models by using Amazon Bedrock with provisioned throughput to handle peak loads. Configure the number of model units (MUs) based on expected token throughput needs. Implement request batching for each model. 
D. Deploy each model to a separate Amazon SageMaker AI endpoint. Use an asynchronous inference configuration. Store model requests and responses in Amazon S3. Use Amazon SNS to send alerts to users when the application finishes processing requests. 

Show / Hide Answer

---

Question # 12

A financial services company is developing an AI-powered search assistant application to help investment advisors quickly retrieve investment data. The application runs as an AWS Lambda function. The company is using Amazon Bedrock to develop the application by using an Amazon Bedrock knowledge base that uses Amazon OpenSearch Serverless as its data source. The application agent must manage collections at scale by automatically assigning access permissions to collections and indexes that match a specific pattern. The company uses Amazon Bedrock tools to test the knowledge base. The knowledge base sync process finishes successfully. However, the test reveals a 400 Bad Authorization error from the BedrockAgentRuntime API and a 403 Forbidden error when the test attempts to access OpenSearch Serverless. The company must resolve the permissions issues. Which combination of solutions will meet this requirement? (Select TWO.)

A. Update the Lambda function execution role to include the bedrock:InvokeAgent permission. Add the aoss:APIAccessAll permission to the Lambda execution role. 
B. Create an OpenSearch Serverless data access policy that includes pattern-based resource rules. 
C. Configure a VPC endpoint policy for OpenSearch Serverless. Add the endpoint to the Lambda function's VPC configuration. 
D. Configure AWS Secrets Manager to store OpenSearch Serverless credentials. Grant the Lambda function access to retrieve the credentials. 
E. Enable IAM authentication for the OpenSearch Serverless domain. Add the es:ESHttp* permission to the Lambda function execution role. 

Show / Hide Answer

---

Question # 13

A company is using Amazon Bedrock to develop an AI-powered application that uses a foundation model (FM) that supports cross-Region inference and provisioned throughput. The application must serve users in Europe and North America with consistently low latency. The application must comply with data residency regulations that require European user data to remain within Europe-based AWS Regions. During testing, the application experiences service degradation when Regional traffic spikes reach service quotas. The company needs a solution that maintains application resilience and minimizes operational complexity. Which solution will meet these requirements?

A. Deploy separate Amazon Bedrock instances in North American and European Regions. Use a custom routing layer that directs traffic based on user location. Configure Amazon CloudWatch alarms to monitor Regional service usage. Use Amazon SNS to send email alerts when usage approaches thresholds. 
B. Use Amazon Bedrock cross-Region inference profiles by specifying geographical codes in profile IDs when calling the InvokeModel API. Configure separate Amazon API Gateway HTTP APIs to direct European and North American users to the appropriate Regional endpoints. 
C. Deploy a multi-Region Amazon API Gateway HTTP API and AWS Lambda functions that implement retry logic to handle throttling. Configure the Lambda functions to call the FM in the nearest secondary Region when quotas are reached. 
D. Configure provisioned throughput for Amazon Bedrock in multiple Regions. Implement failover logic in application code to switch Regions when throttling occurs. Use AWS Global Accelerator to route traffic based on user location. 

Show / Hide Answer

---

Question # 14

A company is building a multicloud generative AI (GenAI)-powered secret resolution application that uses Amazon Bedrock and Agent Squad. The application resolves secrets from multiple sources, including key stores and hardware security modules (HSMs). The application uses AWS Lambda functions to retrieve secrets from the sources. The application uses AWS AppConfig to implement dynamic feature gating. The application supports secret chaining and detects secret drift. The application handles short-lived and expiring secrets. The application also supports prompt flows for templated instructions. The application uses AWS Step Functions to orchestrate agents to resolve the secrets and to manage secret validation and drift detection. The company finds multiple issues during application testing. The application does not refresh expired secrets in time for agents to use. The application sends alerts for secret drift, but agents still use stale data. Prompt flows within the application reuse outdated templates, which cause cascading failures. The company must resolve the performance issues. Which solution will meet this requirement? 

A. Use Step Functions Map states to run agent workflows in parallel. Pass updated secret metadata through Lambda function outputs. Use AWS AppConfig to version all prompt flows to gate and roll back faulty templates. 
B. Use Amazon Bedrock Agents only. Configure Amazon Bedrock guardrails to restrict prompt variation. Use an inline JSON schema for a single agent’s workflow definition to chain tool calls. 
C. Use a centralized Amazon EventBridge pipeline to invoke each agent. Store intermediate prompts in Amazon DynamoDB. Resolve agent ordering by using TTL-based backoff and retries. 
D. Use Amazon EventBridge Pipes to invoke resolvers based on Amazon CloudWatch log patterns. Store response metadata in DynamoDB with TTL and versioned writes. Use Amazon Q Developer to dynamically generate fallback prompts. 

Show / Hide Answer

---

Question # 15

A company is designing a solution that uses foundation models (FMs) to support multiple AI workloads. Some FMs must be invoked on demand and in real time. Other FMs require consistent high-throughput access for batch processing. The solution must support hybrid deployment patterns and run workloads across cloud infrastructure and on-premises infrastructure to comply with data residency and compliance requirements. Which combination of steps will meet these requirements? (Select TWO.)

A. Use AWS Lambda to orchestrate low-latency FM inference by invoking FMs hosted on Amazon SageMaker AI asynchronous endpoints. 
B. Configure provisioned throughput in Amazon Bedrock to ensure consistent performance for high-volume workloads. 
C. Deploy FMs to Amazon SageMaker AI endpoints with support for edge deployment by using Amazon SageMaker Neo. Orchestrate the FMs by using AWS Lambda to support hybrid deployment. 
D. Use Amazon Bedrock with auto-scaling to handle unpredictable traffic surges. E. Use Amazon SageMaker JumpStart to host and invoke the FMs. 

Show / Hide Answer

---

Question # 16

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? 

A. Use 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. 
B. Use 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. 
C. Deploy 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. 
D. Use 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. 

Show / Hide Answer

---

Question # 17

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? 

A. Deploy 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. Manually track compliance certifications across Regions. 
B. Use SCPs with AWS Organizations to manage location-specific permissions. Use AWS CloudTrail immutable logs to audit decision-making processes. Import a custom model into Amazon Bedrock and deploy the model to each Region. 
C. Use 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. 
D. Create separate AWS accounts for each Region with individual compliance frameworks. Use Amazon SageMaker AI with custom monitoring. Create manual compliance reports for each regulatory jurisdiction. 

Show / Hide Answer

---

Question # 18

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 Service Control Policies (SCPs) to allow its accounts to use only the eu-north-1 and eu-west-1 Regions. 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 within the same Regions as the FM. Which solution will meet these requirements?

A. Deploy 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 eucentral-1 and eu-west-3. Extend existing SCPs to allow employees to use the FM. Integrate the REST API with the business application. 
B. Deploy 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. 
C. Configure the FM to use cross-Region inference through a Europe-scoped endpoint. 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. 
D. Deploy the FM in Amazon SageMaker in eu-north-1. Configure a SageMaker VPC endpoint. Extend existing SCPs to allow employees to use the SageMaker endpoint. Integrate the FM in SageMaker with the business application. 

Show / Hide Answer

---

Question # 19

A financial services company is developing a customer service AI assistant application that uses a foundation model (FM) in Amazon Bedrock. The application must provide transparent responses by documenting reasoning and by citing sources that are used for Retrieval Augmented Generation (RAG). The application must capture comprehensive audit trails for all responses to users. The application must be able to serve up to 10,000 concurrent users and must respond to each customer inquiry within 2 seconds. Which solution will meet these requirements with the LEAST operational overhead?

A. Enable tracing for Amazon Bedrock Agents. Configure structured prompts that direct the FM to provide evidence presentations. Integrate Amazon Bedrock Knowledge Bases with data sources to enable RAG. Configure the application to reference and cite authoritative content. Deploy the application in a Multi-AZ architecture. Use Amazon API Gateway and AWS Lambda functions to scale the application. Use Amazon CloudFront to provide lowlatency delivery. 
B. Enable tracing for Amazon Bedrock agents. Integrate a custom RAG pipeline with Amazon OpenSearch Service to retrieve and cite sources. Configure structured prompts to present retrieved evidence. Deploy the application behind an Amazon API Gateway REST API. Use AWS Lambda functions and Amazon CloudFront to scale the application and to provide low latency. Store logs in Amazon S3 and use AWS CloudTrail to capture audit trails. 
C. Use Amazon CloudWatch to monitor latency and error rates. Embed model prompts directly in the application backend to cite sources. Store application interactions with users in Amazon RDS for audits. 
D. Store generated responses and supporting evidence in an Amazon S3 bucket. Enable versioning on the bucket for audits. Use AWS Glue to catalog retrieved documents. Process the retrieved documents in Amazon Athena to generate periodic compliance reports. 

Show / Hide Answer

---

Question # 20

A media company must use Amazon Bedrock to implement a robust governance process for AI-generated content. The company needs to manage hundreds of prompt templates. Multiple teams use the templates across multiple AWS Regions to generate content. The solution must provide version control with approval workflows that include notifications for pending reviews. The solution must also provide detailed audit trails that document prompt activities and consistent prompt parameterization to enforce quality standards. Which solution will meet these requirements? 

A. Configure Amazon Bedrock Studio prompt templates. Use Amazon CloudWatch dashboards to display prompt usage metrics. Store approval status in Amazon DynamoDB. Use AWS Lambda functions to enforce approvals. 
B. Use Amazon Bedrock Prompt Management to implement version control. Configure AWS CloudTrail for audit logging. Use AWS Identity and Access Management policies to control approval permissions. Create parameterized prompt templates by specifying variables. 
C. Use AWS Step Functions to create an approval workflow. Store prompts in Amazon S3. Use tags to implement version control. Use Amazon EventBridge to send notifications. 
D. Deploy Amazon SageMaker Canvas with prompt templates stored in Amazon S3. Use AWS CloudFormation for version control. Use AWS Config to enforce approval policies. 

Show / Hide Answer

---

<12345>

---

Download All 128 Questions Check Customers Feedbacks