A company is building a generative AI (GenAI) application that uses Amazon Bedrock APIs to process complex customer inquiries. During peak usage periods, the application experiences intermittent API timeouts that cause issues such as broken response chunks and delayed data delivery. The application struggles to ensure that prompts remain within token limits when handling complex customer inquiries of varying lengths. Users have reported truncated inputs and incomplete responses. The company has also observed foundation model (FM) invocation failures.
The company needs a retry strategy that automatically handles transient service errors and prevents overwhelming Amazon Bedrock during peak usage periods. The strategy must also adapt to changing service availability and support response streaming and token-aware request handling.
Which solution will meet these requirements?
A. Implement a standard retry strategy that uses a 1-second fixed delay between attempts and a 3-retry maximum for all errors. Handle streaming response timeouts by restarting streams. Cap token usage for each session.
B. Implement an adaptive retry strategy that uses exponential backoff with jitter and a circuit breaker pattern that temporarily disables retries when error rates exceed a predefined threshold. Implement a streaming response handler that monitors for chunk delivery timeouts. Configure the handler to buffer successfully received chunks and intelligently resume streaming from the last received chunk when connections are re-established.
C. Use the AWS SDK to configure a retry strategy in standard mode. Wrap Amazon Bedrock API calls in try-catch blocks that handle timeout exceptions. Return cached completions for failed streaming requests. Enforce a global token limit for all users. Add jitter-based retry logic and lightweight token trimming for each request. Resume broken streams by requesting only the missing chunks from the point of failure. Maintain a small in-memory buffer of the most recent chunks to minimize redundant data transfer.
D. Set Amazon Bedrock client request timeouts to 30 seconds. Implement client-side load shedding. Buffer partial results and stop new requests when the application performance begins to degrade. Set static token usage caps for all requests. Configure exponential backoff retries, dynamic chunk sizing, and context-aware token limits.
Show Answer
Correct Answer: B
Explanation: An adaptive retry strategy with exponential backoff and jitter is the recommended approach for transient errors and throttling because it reduces synchronized retries and adapts to changing service conditions. Adding a circuit breaker prevents overwhelming the service during sustained failures. A streaming handler that buffers received chunks and resumes from the last successful point best addresses interrupted streaming responses. This option most completely satisfies the requirements for adaptive retries, response streaming resilience, and token-aware handling compared with the alternatives, which rely on static retry behavior or include capabilities not supported as described.
Question 32
A healthcare company is developing a document management system that stores medical research papers in an Amazon S3 bucket. The company needs to build a comprehensive metadata framework that will improve search precision for a generative AI (GenAI) application that analyzes the research papers. The metadata framework must include document timestamps, author information, and research domain classifications.
The solution must maintain a consistent metadata structure across all uploaded documents. The solution must give foundation models (FMs) the ability to understand document context without accessing the full content.
Which solution will meet these requirements?
A. Store document timestamps in Amazon S3 system metadata. Use S3 object tags to implement domain classification. Implement custom user-defined metadata to store author information.
B. Set up S3 Object Lock with legal holds to track document timestamps. Use S3 object tags to store author information. Implement S3 access points for domain classification.
C. Use S3 Inventory reports to track document timestamps. Create S3 access points to implement domain classification. Store author information in S3 Storage Lens dashboards.
D. Use custom user-defined metadata to store author information. Use S3 Object Lock retention periods to track document timestamps. Use S3 Event Notifications to implement domain classification.
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Correct Answer: A
Explanation: Amazon S3 system metadata includes object timestamps such as Last-Modified. User-defined metadata is appropriate for descriptive attributes like author information, while S3 object tags are designed for categorical classifications such as research domains. Together these provide a consistent metadata structure that downstream AI systems can inspect without reading full document contents.
Question 33
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?
A. Create 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.
B. Use 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.
C. Implement 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.
D. Create 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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Correct Answer: B
Explanation: Amazon Bedrock Intelligent Prompt Routing is designed to automatically route requests to the most appropriate model based on the prompt, optimizing cost, latency, and quality with minimal implementation effort. It avoids building custom classification and routing logic while meeting the requirement to direct simple inquiries to smaller models and complex inquiries to more capable models.
Question 34
A hotel company wants to enhance a legacy Java-based property management system (PMS) by adding AI capabilities. The company wants to use Amazon Bedrock Knowledge Bases to provide staff with room availability information and hotel-specific details. The solution must maintain separate access controls for each hotel that the company manages. The solution must provide room availability information in near real time and must maintain consistent performance during peak usage periods.
Which solution will meet these requirements?
A. Deploy a single Amazon Bedrock knowledge base that contains combined data for all hotels. Configure AWS Lambda functions to synchronize data from each hotel's PMS database through direct API connections. Implement AWS CloudTrail logging with hotel-specific filters to audit access logs for each hotel's data.
B. Create an Amazon EventBridge rule for each hotel that is invoked by changes to the PMS database for each hotel. Configure the rule to send updates to a centralized Amazon Bedrock knowledge base in a management AWS account. Configure resource-based policies to enforce hotel-specific access controls for hotel staff.
C. Implement one Amazon Bedrock knowledge base for each hotel in a multi-account structure. Use direct data ingestion to provide real-time room availability information. Schedule regular synchronization for less critical information.
D. Build a centralized Amazon Bedrock agent that uses multiple knowledge bases. Implement AWS IAM Identity Center with hotel-specific permission sets to control hotel staff data access.
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Correct Answer: C
Explanation: Using a separate Amazon Bedrock knowledge base for each hotel in a multi-account architecture provides strong tenant isolation and independent access controls. It also scales with service quotas. Keeping rapidly changing room availability synchronized through direct ingestion while scheduling periodic synchronization for less frequently changing hotel information best matches the requirement for near real-time availability and consistent performance.
Question 35
A company wants to create an annual rewards program for its customers. The rewards that customers earn vary based on different parameters such as the categories of the items ordered and the customers' purchase history.
The company needs a generative AI (GenAI) solution that uses three Amazon Bedrock agents to help customers during online catalog browsing. The agents must use knowledge bases and action groups to handle the search, recommendation, and order modules. The modules must operate sequentially. An AWS Lambda function must calculate estimated rewards for each recommended item. The solution must provide graceful degradation during service disruptions.
Which solution will meet these requirements with the MOST operational efficiency?
A. Define an Amazon API Gateway REST API behind each agent. Create a second Lambda function to orchestrate the calls to the agents and the rewards Lambda function. Configure the second Lambda function with a retry/fallback mechanism.
B. Create an AWS Step Functions state machine with four tasks that run the agents and the rewards Lambda function. Set up retry and catch branches for each of the task steps.
C. Configure each agent with a separate retry/fallback mechanism. Create a second Lambda function to orchestrate the calls to the agents and the rewards Lambda function. Define an Amazon API Gateway REST API behind the second Lambda function.
D. Create a second Lambda function to orchestrate the calls to the agents and the rewards Lambda function. Create an AWS Step Functions state machine with one task that runs the second Lambda function. Set up retry and catch branches for the task step.
Show Answer
Correct Answer: B
Explanation: AWS Step Functions is the most operationally efficient choice for orchestrating sequential execution of three Amazon Bedrock agents and a Lambda function. It provides native workflow orchestration, state management, retries, and catch/fallback handling for each step without requiring custom orchestration code. This supports graceful degradation during service disruptions while minimizing operational overhead compared to Lambda-based orchestration.
Question 36
A company is building a serverless application that uses AWS Lambda functions to help students around the world summarize notes. The application uses Anthropic Claude through Amazon Bedrock. The company observed that most of the traffic occurs during evenings in each time zone. Users report experiencing throttling errors during peak usage times in their times zones.
The company needs to resolve the throttling issues by ensuring continuous operation of the application. The solution must maintain application performance quality. The company needs a solution that does not require a fixed hourly cost during low traffic periods.
Which solution will meet these requirements?
A. Create custom Amazon CloudWatch metrics to monitor model errors. Set provisioned throughput to a value that is safely higher than the peak traffic observed.
B. Create custom Amazon CloudWatch metrics to monitor model errors. Set up a failover mechanism to redirect invocations to a backup AWS Region when the errors exceed a specified threshold.
C. Enable invocation logging in Amazon Bedrock. Monitor key metrics such as Invocations, InputTokenCount, OutputTokenCount, and Invocation Throttles. Distribute traffic across cross-Region inference endpoints.
D. Enable invocation logging in Amazon Bedrock. Monitor InvocationLatency, InvocationClientErrors, and InvocationServerErrors metrics. Distribute traffic across multiple versions of the same model.
Show Answer
Correct Answer: C
Explanation: Cross-Region inference in Amazon Bedrock is designed to distribute inference requests across multiple AWS Regions using on-demand capacity, reducing throttling during regional peak demand while maintaining performance. It avoids the fixed hourly cost of Provisioned Throughput. Monitoring invocation metrics and enabling invocation logging helps observe throttles and usage. Provisioned Throughput incurs reserved capacity costs, simple regional failover only reacts after failures rather than balancing load, and distributing across model versions does not address regional capacity throttling.
Question 37
A financial services company needs to pre-process unstructured data such as customer transcripts, financial reports, and documentation. The company stores the unstructured data in Amazon S3 to support an Amazon Bedrock application.
The company must validate data quality, create auditable metadata, monitor data metrics, and customize text chunking to optimize foundation model (FM) performance.
Which solution will meet these requirements with the LEAST development effort?
A. Use Amazon SageMaker Data Wrangler to create a data flow. Configure Amazon CloudWatch metrics and alarms to monitor data quality. Use a custom AWS Lambda function to pre-process the data. Load processed data into Amazon Bedrock.
B. Set up an AWS Glue crawler to catalog data sources. Create AWS Glue ETL jobs to run custom transformation scripts. Use AWS Glue Data Quality to validate and monitor data quality. Load processed data into Amazon Bedrock.
C. Use Amazon Comprehend to extract entities. Create an AWS Lambda function to chunk text. Run Amazon Athena to query and validate data quality. Load processed data into Amazon Bedrock.
D. Create an AWS Step Functions workflow to orchestrate data pre-processing tasks. Run custom code on Amazon EC2 instances to process the data. Use Amazon SageMaker Model Monitor to monitor data quality. Load processed data into Amazon Bedrock.
Show Answer
Correct Answer: B
Explanation: AWS Glue provides the most integrated low-code solution for preprocessing data stored in Amazon S3. AWS Glue crawlers catalog unstructured data, Glue ETL jobs perform custom transformations (including text chunking), and AWS Glue Data Quality validates, profiles, monitors data quality, and supports metadata/cataloging. This satisfies the requirements with the least development effort compared to building custom Lambda, EC2, or SageMaker-based pipelines.
Question 38
A financial services company is developing a generative AI (GenAI) application that serves both premium customers and standard customers. The application uses AWS Lambda functions behind an Amazon API Gateway REST API to process requests. The company needs to dynamically switch between AI models based on which customer tier each user belongs to. The company also wants to perform A/B testing for new features without redeploying code. The company needs to validate model parameters like temperature and maximum token limits before applying changes.
Which solution will meet these requirements with the LEAST operational overhead?
A. Create an AWS Systems Manager Parameter Store parameters for each configuration. Use Lambda functions to poll for parameter updates. Use Amazon EventBridge events to trigger redeployments when configurations change.
B. Store model configurations in Amazon DynamoDB tables. Optimize access patterns to retrieve configurations according to customer tier. Configure Lambda functions to query DynamoDB at the beginning of each request to determine which model to use.
C. Use AWS AppConfig to manage model configurations. Use feature flags to perform A/B testing. Define JSON schema validation rules for model parameters. Configure Lambda functions to retrieve configurations by using the AWS AppConfig Agent.
D. Create an Amazon ElastiCache (Redis OSS) cluster to store model configurations. Set short TTL values. Run custom validation logic in Lambda functions. Use Amazon CloudWatch metrics to monitor configuration usage.
Show Answer
Correct Answer: C
Explanation: AWS AppConfig is purpose-built for dynamic application configuration with minimal operational overhead. It supports feature flags for A/B testing, configuration deployment without code redeployment, JSON schema validation to validate parameters such as temperature and maximum token limits, and integrates with Lambda via the AWS AppConfig Agent or extension for efficient configuration retrieval and caching.
Question 39
A financial technology company is using Amazon Bedrock to build an assessment system for the company's customer service AI assistant. The AI assistant must provide financial recommendations that are factually accurate, compliant with financial regulations, and conversationally appropriate. The company needs to combine automated quality evaluations at scale with targeted human reviews of critical interactions.
What solution will meet these requirements?
A. Configure a pipeline in which financial experts manually score all responses for accuracy, compliance, and conversational quality. Use Amazon SageMaker notebooks to analyze results to identify improvement areas.
B. Configure Amazon Bedrock evaluations that use Anthropic Claude Sonnet as a judge model to assess response accuracy and appropriateness. Configure custom Amazon Bedrock guardrails to check responses for compliance with financial policies. Add Amazon Augmented AI (Amazon A2I) human reviews for flagged critical interactions.
C. Create an Amazon Lex bot to manage the customer service interactions. Configure AWS Lambda functions to check responses against a static compliance database. Configure intents in the bot that call the Lambda functions to check the responses. Add an additional intent to collect end-user reviews.
D. Configure Amazon CloudWatch to monitor response patterns from the AI assistant. Configure CloudWatch alerts for potential compliance violations. Establish a team of human evaluators to review flagged interactions.
Show Answer
Correct Answer: B
Explanation: Amazon Bedrock evaluations can use a judge model such as Claude Sonnet to automatically assess qualities like factual accuracy and conversational appropriateness at scale. Bedrock Guardrails can enforce custom policy checks relevant to financial compliance. Amazon Augmented AI (A2I) adds targeted human review for flagged or high-risk interactions, satisfying the requirement to combine scalable automated evaluation with human oversight.
Question 40
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.
Which 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.
Show Answer
Correct Answer: B, C
Explanation: Amazon Bedrock publishes native token and invocation metrics to Amazon CloudWatch, making CloudWatch dashboards and alarms the lowest-overhead way to achieve real-time visibility and alerting. For deeper observability, enabling Bedrock invocation logging to CloudWatch Logs and querying with CloudWatch Logs Insights provides token consumption analysis, attribution by application, and custom log widgets for dashboards. QuickSight is not the native dashboarding solution for CloudWatch metrics, the Managed Grafana zero-ETL option is not a standard Bedrock capability, and the EventBridge/Firehose/OpenSearch pipeline introduces unnecessary operational overhead.
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