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Questions 41–50 of the latest 50 AIP questions (5 pages total), with answers and explanations, no login required. Practice all 98 questions online.

Question 41 · No. 88 · Single choice

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.\nThe 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.\nWhich 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 and data processing activities. Manually track compliance certifications across Regions.
B. Use 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.
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 to track model performance and compliance with data residency requirements. Create manual reports for each regulatory jurisdiction.

Answer: C

Explanation: Option C is the best choice because it addresses all the requirements: (1) Data residency is maintained by pre-processing data within the Region based on geographic origin before sending to Amazon Bedrock, and using Region-specific S3 bucket policies; (2) Amazon Macie is specifically designed for data classification capabilities, which is a key requirement; (3) AWS CloudTrail immutable logs provide the required audit trails for decision-making processes; (4) S3 Object Lock helps with compliance and data protection regulations. Option A fails because cross-Region inference would violate data residency requirements. Option B lacks data classification capabilities. Option D uses SageMaker instead of Bedrock (which is specified in the requirements) and relies on manual reports.

Question 42 · No. 89 · Single choice

An international company is building an AI assistant that uses RAG. The company wants the AI assistant to have near real-time, low-latency performance. The AI assistant must provide service to several geographic areas. The company's customers will use proprietary data with the AI assistant. The proprietary data must not leave the company's immediate geographic area.\nWhich solution will meet these requirements?

A. Deploy an Amazon Bedrock model with a cross-Region model inference profile. Create Amazon S3 buckets in each AWS Region the company operates in. Store a knowledge base in each respective S3 bucket. In each Region, configure Amazon Kendra to interact with the respective knowledge base. In each Region, configure an AWS Lambda function that uses Kendra and Amazon Bedrock to process AI assistant prompts.
B. Deploy an Amazon Bedrock model in each AWS Region the company operates in. Configure an Amazon Bedrock cross-Region model inference profile. Configure a vector database that uses Amazon Bedrock Knowledge Bases. Store the knowledge bases in Amazon S3 in each Region the company operates in.
C. Use AWS Outposts to deploy an outpost in each AWS Region the company operates in. Create Amazon S3 buckets to store knowledge bases in each corresponding Region. Deploy Amazon RDS configured as a vector database to each outpost. Deploy an Amazon Bedrock model with a cross-Region inference profile in a central Region.
D. Configure a knowledge base stored in the Amazon S3 Express One Zone storage class in each AWS Local Zone the company operates in. Use Amazon RDS to deploy a vector database in each Local Zone the company operates in. Deploy a large language model (LLM) to Amazon EC2 instances in each Local Zone. Configure the AI assistant to route prompts to the model in the respective Local Zone.

Answer: D

Explanation: Option D is the correct answer because it best meets all the requirements. The key requirements are: 1) near real-time, low-latency performance, 2) service to several geographic areas, 3) proprietary data must not leave the company's immediate geographic area. Local Zones are AWS infrastructure placed closer to end users in specific geographic locations. By configuring knowledge bases in S3 Express One Zone within each Local Zone, deploying RDS as vector databases in each Local Zone, and deploying LLMs to EC2 instances in each Local Zone, all data processing and storage stays within the immediate geographic area. This ensures data residency requirements are met while providing low-latency performance due to local processing. Options A, B, and C all involve cross-Region inference profiles, which would potentially route data to different Regions, violating the requirement that proprietary data must not leave the company's immediate geographic area. Option C also incorrectly uses AWS Outposts, which is designed for on-premises deployments rather than multi-Region geographic distribution.

Question 43 · No. 90 · Multiple choice

A company deploys multiple Amazon Bedrock based generative AI (GenAI) applications across multiple business units for customer service, content generation, and document analysis. Some applications show unpredictable token consumption patterns. The company requires a comprehensive observability solution that provides real-time visibility into token usage patterns across multiple models. The observability solution must support custom dashboards for multiple stakeholder groups and provide alerting capabilities for token consumption across all the foundational models that the company's applications use.\nWhich combination of solutions will meet these requirements with the LEAST operational overhead? (Choose two.)

A. Use Amazon CloudWatch metrics as data sources to create custom Amazon QuickSight dashboards that show token usage trends and usage patterns across FMs.
B. Use Amazon CloudWatch Logs Insights to analyze Amazon Bedrock invocation logs for token consumption patterns and usage attribution by application. Create custom queries to identify high-usage scenarios. Add log widgets to dashboards to enable continuous monitoring.
C. Create custom Amazon CloudWatch dashboards that combine native Amazon Bedrock token and invocation CloudWatch metrics. Set up CloudWatch alarms to monitor token usage thresholds.
D. Create dashboards that show token usage trends and patterns across the company's FMs by using an Amazon Bedrock zero-ETL integration with Amazon Managed Grafana.
E. Implement Amazon EventBridge rules to capture Amazon Bedrock model invocation events. Route token usage data to an Amazon Data Firehose delivery stream that targets Amazon OpenSearch Serverless. Use OpenSearch dashboards to analyze usage patterns.

Answer: B, C

Question 44 · No. 91 · Single choice

A company is building a legal research AI assistant that uses Amazon Bedrock with an Anthropic Claude foundation model (FM). The AI assistant must retrieve highly relevant case law documents to augment the FM's responses. The AI assistant must identify semantic relationships between legal concepts, specific legal terminology, and citations. The AI assistant must perform quickly and return precise results.\nWhich solution will meet these requirements?

A. Configure an Amazon Bedrock knowledge base to use a default vector search configuration. Use Amazon Bedrock to expand queries to improve retrieval for legal documents based on specific terminology and citations.
B. Use Amazon OpenSearch service to deploy a hybrid search architecture that combines vector search with keyword search. Apply an Amazon Bedrock reranker model to optimize result relevance.
C. Enable the Amazon Kendra query suggestion feature for end users. Use Amazon Bedrock to perform post-processing of search results to identify semantic similarity in the documents and to produce precise results.
D. Use Amazon OpenSearch Service with vector search and Amazon Bedrock Titan embeddings to index and search legal documents. Use custom AWS Lambda functions to merge results with keyword-based filters that are stored in an Amazon RDS database.

Answer: B

Explanation: Option B is the best choice because it uses Amazon OpenSearch Service to deploy a hybrid search architecture that combines both vector search and keyword search. This addresses all the requirements: vector search captures semantic relationships between legal concepts, while keyword search handles specific legal terminology and citations. Additionally, applying an Amazon Bedrock reranker model optimizes the relevance of results, ensuring precision. OpenSearch is a managed service that provides fast performance. Option A only uses vector search which may miss exact terminology matches. Option C uses Kendra which is more suited for enterprise search and post-processing can introduce latency. Option D requires custom Lambda functions and RDS for keyword filters, adding complexity and potential latency that would impact the quick performance requirement.

Question 45 · No. 92 · Single choice

A financial services company is creating a Retrieval Augmented Generation (RAG) application that uses Amazon Bedrock to generate summaries of market activities. The application relies on a vector database that stores a small proprietary dataset that has a low index count. The application must perform similarity searches. The Amazon Bedrock model's responses must maximize accuracy and maintain high performance.\nThe company needs to configure the vector database and integrate it with the application.\nWhich solution will meet these requirements?

A. Launch an Amazon MemoryDB cluster and configure the index by using the Flat algorithm. Configure a horizontal scaling policy based on performance metrics.
B. Launch an Amazon MemoryDB cluster and configure the index by using the Hierarchical Navigable Small World (HNSW) algorithm. Configure a vertical policy based on performance metrics.
C. Launch an Amazon Aurora PostgresSQL cluster and configure the index by using the Inverted File with Flat Compression (IVFFlat) algorithm. Configure the instance class to scale to a larger size when the load increases.
D. Launch an Amazon DocumentDB cluster that has an Inverted File with Flat Compression (IVFFlat) index and a high probe value. Configure connections to the cluster as a replica set Distribute reads to replica instances.

Answer: A

Explanation: Option A is the best choice because: (1) Amazon MemoryDB supports vector similarity search natively with both FLAT and HNSW algorithms. (2) The FLAT algorithm performs exhaustive brute-force search, providing maximum accuracy (100% recall) for similarity searches. (3) Since the dataset is small with a low index count, the performance impact of exhaustive search is negligible, satisfying the high-performance requirement. (4) Horizontal scaling policy allows the cluster to scale out as the workload grows, maintaining performance. Option B uses HNSW which is approximate (slightly less accurate than FLAT), and vertical scaling has limitations. Options C and D use IVFFlat which requires training data and is not as accurate as FLAT; additionally, Aurora PostgreSQL and DocumentDB are not natively optimized for vector search compared to MemoryDB.

Question 46 · No. 93 · Single choice

A company is developing a generative AI (GenAI) application that uses Amazon Bedrock foundation models (FMs). The application has several custom tool integrations. The application has experienced unexpected token consumption surges despite consistent user traffic.\nThe company needs a solution that uses Amazon Bedrock model invocation logging to monitor InputTokenCount metrics and OutputTokenCount metrics. The solution must detect unusual patterns in tool usage and identify which specific tool integrations cause abnormal token consumption. The solution must also automatically adjust thresholds as traffic patterns change.\nWhich solution will meet these requirements?

A. Use Amazon CloudWatch Logs to capture model invocation logs. Create CloudWatch dashboards based on InputTokenCount metrics and OutputTokenCount metrics. Configure static CloudWatch alarms with fixed thresholds for each tool integration.
B. Store model invocation logs in an Amazon S3 bucket. Use AWS Glue to catalog the logs. Analyze token consumption patterns by using scheduled Amazon Athena queries that generate reports on tool usage trends.
C. Use Amazon CloudWatch Logs to capture model invocation logs. Create CloudWatch metric filters to extract tool-specific invocation patterns. Apply CloudWatch anomaly detection alarms that adjust baselines for each tool's metrics.
D. Store model invocation logs in an Amazon S3 bucket. Create an AWS Lambda function to process logs in real time. Manually update Amazon CloudWatch alarm thresholds based on token consumption trends that the Lambda function identifies.

Answer: C

Explanation: Option C is the correct choice because it uses CloudWatch Logs to capture model invocation logs, creates metric filters to extract tool-specific invocation patterns, and applies CloudWatch anomaly detection alarms that automatically adjust baselines for each tool's metrics. This directly addresses all requirements: monitoring InputTokenCount and OutputTokenCount metrics, detecting unusual patterns in tool usage, identifying specific tool integrations causing abnormal token consumption, and automatically adjusting thresholds as traffic patterns change. Option A uses static thresholds that don't adapt to changing patterns. Option B relies on scheduled Athena queries which don't provide real-time anomaly detection or automatic threshold adjustment. Option D requires manual threshold updates, which doesn't meet the automatic adjustment requirement.

Question 47 · No. 94 · Single choice

A company is planning to deploy multiple generative AI (GenAI) applications to five independent business units that operate in multiple countries in Europe and the Americas. Each application uses Amazon Bedrock Retrieval Augmented Generation (RAG) patterns with business unit-specific knowledge bases that store terabytes of unstructured data.\nThe company must establish well-architected, standardized components for security controls, observability practices, and deployment patterns across all the GenAI applications. The components must be reusable, versioned, and governed consistently.\nWhich solution will meet these requirements?

A. Configure Amazon API Gateway REST API endpoints for the GenAI applications. Deploy common security, observability, and RAG patterns based on the AWS Well-Architected Generative AI Lens in standardized AWS CloudFormation templates. Use CloudFormation Guard after the deployment to validate policy compliance in each business unit.
B. Create standardized AWS CloudFormation templates to implement security, observability, and RAG patterns based on the AWS Well-Architected Generative AI Lens. Establish a centralized repository that performs version control. Integrate a CI/CD pipeline with CloudFormation Guard to enforce consistent and repeatable deployments across business units.
C. Use AWS Service Catalog to define standardized portfolios and versioned products for each business unit. Use the portfolios to enforce security, observability, and RAG patterns based on the AWS Well-Architected Generative AI Lens. Require the business units to use the Service Catalog console to deploy resources.
D. Document security controls, observability requirements, and RAG patterns based on the AWS Well-Architected Generative AI Lens in a shared design document. Use Amazon Macie to enforce deployment. Delegate implementation responsibility to each business unit.

Answer: B

Explanation: Option B best meets all the requirements. It provides standardized AWS CloudFormation templates for implementing security, observability, and RAG patterns based on the AWS Well-Architected Generative AI Lens. The centralized repository enables version control, making components reusable and versioned. The CI/CD pipeline integrated with CloudFormation Guard ensures consistent and repeatable deployments across all business units, providing proactive governance rather than reactive validation. Option A only validates after deployment (reactive), Option C requires manual console-based deployment which doesn't scale well for multiple business units and CI/CD automation, and Option D uses Amazon Macie (which is for data security/classification) and delegates implementation, which lacks consistency and standardization.

Question 48 · No. 95 · Single choice

A company uses AWS Lambda functions to build an AI agent solution. A GenAI developer must set up a Model Context Protocol (MCP) server that accesses user information. The GenAI developer must also configure the AI agent to use the new MCP server. The GenAI developer must ensure that only authorized users can access the MCP server.\nWhich solution will meet these requirements?

A. Use a Lambda function to host the MCP server. Grant the AI agent Lambda functions permission to invoke the Lambda function that hosts the MCP server. Configure the AI agent's MCP client to invoke the MCP server asynchronously.
B. Use a Lambda function to host the MCP server. Grant the AI agent Lambda functions permission to invoke the Lambda function that hosts the MCP server. Configure the AI agent to use the STDIO transport with the MCP server.
C. Use a Lambda function to host the MCP server. Create an Amazon API Gateway HTTP API that proxies requests to the Lambda function. Configure the AI agent solution to use the Streamable HTTP transport to make requests through the HTTP API. Use Amazon Cognito to enforce OAuth 2.1.
D. Use a Lambda layer to host the MCP server. Add the Lambda layer to the AI agent Lambda functions. Configure the agentic AI solution to use the STDIO transport to send requests to the MCP server. In the AI agent's MCP configuration, specify the Lambda layer ARN as the command. Specify the user credentials as environment variables.

Answer: C

Explanation: Option C is the correct solution because it addresses all three requirements: (1) It uses a Lambda function to host the MCP server, which is appropriate for serverless architectures; (2) It uses an API Gateway HTTP API with the Streamable HTTP transport, which is the standard transport protocol for remote MCP servers and allows the AI agent to communicate with the MCP server over HTTP; (3) Most importantly, it uses Amazon Cognito to enforce OAuth 2.1, which ensures that only authorized users can access the MCP server. Options A and B fail to address the authorization requirement, and STDIO transport (used in B and D) is only suitable for local processes, not for remote serverless deployments. Option D incorrectly suggests using a Lambda layer to host a server, but Lambda layers are simply code libraries/dependencies, not executable servers.

Question 49 · No. 96 · Single choice

A company has a recommendation system. The system's applications run on Amazon EC2 instances. The applications make API calls to Amazon Bedrock foundation models (FMs) to analyze customer behavior and generate personalized product recommendations.\nThe system is experiencing intermittent issues. Some recommendations do not match customer preferences. The company needs an observability solution to monitor operational metrics and detect patterns of operational performance degradation compared to established baselines. The solution must also generate alerts with correlation data within 10 minutes when FM behavior deviates from expected patterns.\nWhich solution will meet these requirements?

A. Configure Amazon CloudWatch Container Insights for the application infrastructure. Set up CloudWatch alarms for latency thresholds. Add custom metrics for token counts by using the CloudWatch embedded metric format. Create CloudWatch dashboards to visualize the data.
B. Implement AWS X-Ray to trace requests through the application components. Enable CloudWatch Logs Insights for error pattern detection. Set up AWS CloudTrail to monitor all API calls to Amazon Bedrock. Create custom dashboards in Amazon QuickSight.
C. Enable Amazon CloudWatch Application Insights for the application resources. Create custom metrics for recommendation quality, token usage, and response latency by using the CloudWatch embedded metric format with dimensions for request types and user segments. Configure CloudWatch anomaly detection on the model metrics. Establish log pattern analysis by using CloudWatch Logs Insights.
D. Use Amazon OpenSearch Service with the Observability plugin. Ingest model metrics and logs by using Amazon Kinesis. Create custom Piped Processing Language (PPL) queries to analyze model behavior patterns. Establish operational dashboards to visualize anomalies in real time.

Answer: C

Explanation: Option C is the correct choice because it directly addresses all the requirements stated in the question. CloudWatch Application Insights is specifically designed to monitor application resources and provides automated dashboards and insights. The custom metrics with dimensions for request types and user segments enable correlation data analysis, which is required for understanding operational performance. Most importantly, CloudWatch anomaly detection is purpose-built to detect deviations from established baselines, which directly matches the requirement of detecting patterns of operational performance degradation. Additionally, CloudWatch Logs Insights provides log pattern analysis capabilities. This solution can generate alerts within the 10-minute timeframe when FM behavior deviates from expected patterns. Options A and B don't focus on detecting deviations from baselines, while Option D uses a more complex setup that may not reliably meet the 10-minute alert requirement.

Question 50 · No. 97 · Single choice

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?

A. Configure 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.
B. Configure 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.
C. Configure 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.
D. Configure 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.

Answer: A

Explanation: Option A is the correct choice because it addresses all requirements: (1) It uses a global inference profile to enable cross-Region routing, (2) It creates a dedicated European inference profile with Geographic cross-Region inference specifically for European applications, which ensures data sovereignty by keeping European customer data within European AWS Regions, (3) It uses Route 53 health checks for automated failover during connectivity disruptions to maintain high availability, and (4) CloudWatch alarms provide monitoring. Option D fails because cross-Region failover mechanisms could route European traffic to non-European Regions, violating data sovereignty. Option B doesn't specifically address data sovereignty. Option C uses IAM roles which only control permissions, not data residency, and uses on-demand throughput which cannot guarantee sub-500ms response times at 1,000 TPS, plus ResponseStream adds unnecessary latency for fraud detection.

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