AWS AIP-C01 Practice Questions with Explanations

Q: A retail company has a generative AI (GenAI) product recommendation application that uses Amazon Bedrock. The application suggests products to customers based on browsing history and demographics. The company needs to implement fairness evaluation across multiple demographic groups to detect and mea

B. Option B directly addresses fairness evaluation with the least custom development by using Amazon Bedrock's built-in Prompt Management for variants, Bedrock Flows for traffic allocation, and Bedrock Guardrails specifically designed to monitor demographic fairness through content filters. The guardrails' InvocationsIntervened metrics on GuardrailContentSource dimensions can be configured with CloudWatch alarms for the 15% threshold violation detection. This approach minimizes custom development compared to building custom metrics and Lambda post-processing functions.

Q: A company has deployed an AI assistant as a React application that uses AWS Amplify, an AWS AppSync GraphQL API, and Amazon Bedrock Knowledge Bases. The application uses the GraphQL API to call the Amazon Bedrock RetrieveAndGenerate API for knowledge base interactions. The company configures an AWS

A. AWS Amplify AI Kit provides native support for streaming responses from GraphQL APIs and optimizes client-side rendering for long-running AI operations. This directly addresses the timeout and latency issues caused by complex questions requiring longer processing. The streaming approach allows responses to begin flowing to the client immediately rather than waiting for complete computation, significantly improving perceived performance and user experience without architectural changes.

Q: An ecommerce company operates a global product recommendation system that needs to switch between multiple foundation models (FM) in Amazon Bedrock based on regulations, cost optimization, and performance requirements. The company must apply custom controls based on proprietary business logic, inclu

C. Option C uses AWS AppConfig Agent to fetch routing configurations dynamically for each request, enabling immediate propagation of hourly cost threshold changes and regulatory updates across concurrent requests without code deployment. The Lambda function executes business logic to evaluate user tier, transaction value, regulatory zone, and real- time cost metrics against the dynamically fetched configuration. This architecture supports complex rule evaluation, real-time updates, and model switching without redeployment, meeting all requirements with minimal operational friction.

Q: A company is developing an internal generative AI (GenAI) assistant that uses Amazon Bedrock to summarize corporate documents for multiple business units. The GenAI assistant must generate responses in a consistent format that includes a document summary, classification of business risks, and terms

A. Option A uses Amazon Bedrock Prompt Management to create reusable templates and business unit-specific variants, centralizing management across teams with minimal orchestration overhead. Amazon Bedrock Guardrails with category filters and sensitive term lists provide built-in content moderation without custom post-processing logic. This combination supports consistent formatting, tone adaptation per business unit, and adjustable content moderation criteria over time, all managed centrally with the least maintenance burden.

Q: A financial services company is building a customer support application that retrieves relevant financial regulation documents from a database based on semantic similarities to user queries. The application must integrate with Amazon Bedrock to generate responses. The application must be able to sea

A. Amazon OpenSearch Serverless provides vector search capabilities with built-in metadata filtering for publication date, regulatory agency, and document type across 10 million embeddings. It supports multi-language semantic search (English, Spanish, Portuguese) natively and integrates seamlessly with Amazon Bedrock Knowledge Bases for RAG capabilities using Anthropic Claude models. Serverless architecture minimizes operational overhead while providing low-latency retrieval for real-time customer interactions.

August 14, 2026

Free AWS AIP-C01 practice questions. 30 of them, each with the correct answer, a full explanation, and the reason every other option is wrong. These are real questions from the AIP-C01 exam, not paraphrases, and every explanation is written out rather than just marking the right letter.

They are drawn from the same bank as the full AIP-C01 pack, which has 117 questions in total.

Get the full AIP-C01 question bank (117 questions) →

AIP-C01 practice questions

Question 1

A retail company has a generative AI (GenAI) product recommendation application that uses Amazon Bedrock. The application suggests products to customers based on browsing history and demographics. The company needs to implement fairness evaluation across multiple demographic groups to detect and measure bias in recommendations between two prompt approaches. The company wants to collect and monitor fairness metrics in real time. The company must receive an alert if the fairness metrics show a discrepancy of more than 15% between demographic groups. The company must receive weekly reports that compare the performance of the two prompt approaches. Which solution will meet these requirements with the LEAST custom development effort?

A. Configure an Amazon CloudWatch dashboard to display default metrics from Amazon Bedrock API calls. Create custom metrics based on model outputs. Set up Amazon EventBridge rules to invoke AWS lambda functions that perform post- processing analysis on model responses and publish custom fairness metrics.
B. Create the two prompt variants in Amazon Bedrock Prompt Management. Use Amazon Bedrock Flows to deploy the prompt variants with defined traffic allocation. Configure Amazon Bedrock guardrails that have content filters to monitor demographic fairness. Set up Amazon CloudWatch alarms on the GuardrailContentSource dimension that use InvocationsIntervened metrics to detect recommendation discrepancy threshold violations.
C. Set up Amazon SageMaker Clarify to analyze model outputs. Publish fairness metrics to Amazon CloudWatch. Create CloudWatch composite alarms that combine SageMaker Clarify bias metrics with Amazon Bedrock latency metrics to provide a comprehensive fairness evaluation dashboard.
D. Create an Amazon Bedrock model evaluation job to compare fairness between the two prompt variants. Enable model invocation logging in Amazon CloudWatch. Set up CloudWatch alarms for InvocationsIntervened metrics with a dimension for each demographic group.

Show answer and explanation ▾

Correct answer: B

Option B directly addresses fairness evaluation with the least custom development by using Amazon Bedrock's built-in Prompt Management for variants, Bedrock Flows for traffic allocation, and Bedrock Guardrails specifically designed to monitor demographic fairness through content filters. The guardrails' InvocationsIntervened metrics on GuardrailContentSource dimensions can be configured with CloudWatch alarms for the 15% threshold violation detection. This approach minimizes custom development compared to building custom metrics and Lambda post-processing functions.

Why the other options are wrong:

A. Requires significant custom development building post-processing analysis functions and custom fairness metrics calculation with Lambda, increasing maintenance burden.
C. SageMaker Clarify is designed for one-time model evaluation jobs, not real-time monitoring with alerts, and requires manual integration work rather than native Bedrock guardrail support.
D. Model evaluation jobs are batch-oriented for comparing variants, not suited for real- time metric collection and alerting on fairness discrepancies.

Question 2

A company has deployed an AI assistant as a React application that uses AWS Amplify, an AWS AppSync GraphQL API, and Amazon Bedrock Knowledge Bases. The application uses the GraphQL API to call the Amazon Bedrock RetrieveAndGenerate API for knowledge base interactions. The company configures an AWS Lambda resolver to use the RequestResponse invocation type. Application users report frequent timeouts and slow response times. Users report these problems more frequently for complex questions that require longer processing. The company needs a solution to fix these performance issues and enhance the user experience. Which solution will meet these requirements?

A. Use AWS Amplify AI Kit to implement streaming responses from the GraphQL API and to optimize client-side rendering.
B. Increase the timeout value of the Lambda resolver. Implement retry logic with exponential backoff.
C. Update the application to send an API request to an Amazon SQS queue. Update the AWS AppSync resolver to poll and process the queue.
D. Change the RetrieveAndGenerate API to the InvokeModelWithResponseStream API. Update the application to use an Amazon API Gateway WebSocket API to support the streaming response.

Show answer and explanation ▾

Correct answer: A

AWS Amplify AI Kit provides native support for streaming responses from GraphQL APIs and optimizes client-side rendering for long-running AI operations. This directly addresses the timeout and latency issues caused by complex questions requiring longer processing. The streaming approach allows responses to begin flowing to the client immediately rather than waiting for complete computation, significantly improving perceived performance and user experience without architectural changes.

Why the other options are wrong:

B. Simply increasing timeout values treats the symptom but doesn't solve the underlying slow response problem; exponential backoff on timeouts won't improve performance for intrinsically slow operations.
C. SQS-based asynchronous polling adds significant architectural complexity and latency (polling overhead) compared to native streaming, which is inefficient for real- time user interactions.
D. Switching APIs and adding WebSocket complexity is unnecessarily complicated when Amplify AI Kit provides streaming support natively integrated with existing GraphQL infrastructure.

Question 3

An ecommerce company operates a global product recommendation system that needs to switch between multiple foundation models (FM) in Amazon Bedrock based on regulations, cost optimization, and performance requirements. The company must apply custom controls based on proprietary business logic, including dynamic cost thresholds, AWS Region-specific compliance rules, and real-time A/B testing across multiple FMs. The system must be able to switch between FMs without deploying new code. The system must route user requests based on complex rules including user tier, transaction value, regulatory zone, and real-time cost metrics that change hourly and require immediate propagation across thousands of concurrent requests. Which solution will meet these requirements?

A. Deploy an AWS Lambda function that uses environment variables to store routing rules and Amazon Bedrock FM IDs. Use the Lambda console to update the environment variables when business requirements change. Configure an Amazon API Gateway REST API to read request parameters to make routing decisions.
B. Deploy Amazon API Gateway REST API request transformation templates to implement routing logic based on request attributes. Store Amazon Bedrock FM endpoints as REST API stage variables. Update the variables when the system switches between models.
C. Configure an AWS Lambda function to fetch routing configurations from the AWS AppConfig Agent for each user request. Run business logic in the Lambda function to select the appropriate FM for each request. Expose the FM through a single Amazon API Gateway REST API endpoint.
D. Use AWS Lambda authorizers for an Amazon API Gateway REST API to evaluate routing rules that are stored in AWS AppConfig. Return authorization contexts based on business logic. Route requests to model-specific Lambda functions for each Amazon Bedrock FM.

Show answer and explanation ▾

Correct answer: C

Option C uses AWS AppConfig Agent to fetch routing configurations dynamically for each request, enabling immediate propagation of hourly cost threshold changes and regulatory updates across concurrent requests without code deployment. The Lambda function executes business logic to evaluate user tier, transaction value, regulatory zone, and real- time cost metrics against the dynamically fetched configuration. This architecture supports complex rule evaluation, real-time updates, and model switching without redeployment, meeting all requirements with minimal operational friction.

Why the other options are wrong:

A. Environment variables require Lambda redeployment to update routing rules; they don't support hourly changes or immediate propagation to thousands of concurrent requests.
B. API Gateway request transformation templates cannot handle complex business logic like dynamic cost threshold evaluation and regulatory rule application; stage variables also require manual updates.
D. Using Lambda authorizers for routing logic is an incorrect pattern; authorizers are designed for authentication/authorization, not request routing or business logic execution.

Question 4

A company is developing an internal generative AI (GenAI) assistant that uses Amazon Bedrock to summarize corporate documents for multiple business units. The GenAI assistant must generate responses in a consistent format that includes a document summary, classification of business risks, and terms that are flagged for review. The GenAI assistant must adapt the tone of responses for each user's business unit, such as legal, human resources, or finance. The GenAI assistant must block hate speech, inappropriate topics, and sensitive information such as personal health information. The company needs a solution to centrally manage prompt variants across business units and teams. The company wants to minimize ongoing orchestration efforts and maintenance for post-processing logic. The company also wants to have the ability to adjust content moderation criteria for the GenAI assistant over time. Which solution will meet these requirements with the LEAST maintenance overhead?

A. Use Amazon Bedrock Prompt Management to configure reusable templates and business unit-specific prompt variants. Apply Amazon Bedrock guardrails that have category filters and sensitive term lists to block prohibited content.
B. Use Amazon Bedrock Prompt Management to define base templates. Enforce business unit-specific tone by using system prompt variables. Configure Amazon Bedrock guardrails to apply audience-based threshold tuning. Manage the guardrails by using an internal administration API.
C. Use Amazon Bedrock with business unit-based instruction injection in API calls. Store response formatting rules in Amazon DynamoDB. Use AWS Step functions to validate responses. Use Amazon Comprehend to apply content filters after the GenAI assistant generates responses.
D. Use Amazon Bedrock with custom prompt templates that are stored in Amazon DynamoDB. Create one AWS Lambda function to select business unit-specific prompts. Create a second Lambda function to call Amazon Comprehend to filter prohibited content from responses.

Show answer and explanation ▾

Correct answer: A

Option A uses Amazon Bedrock Prompt Management to create reusable templates and business unit-specific variants, centralizing management across teams with minimal orchestration overhead. Amazon Bedrock Guardrails with category filters and sensitive term lists provide built-in content moderation without custom post-processing logic. This combination supports consistent formatting, tone adaptation per business unit, and adjustable content moderation criteria over time, all managed centrally with the least maintenance burden.

Why the other options are wrong:

B. Audience-based threshold tuning in guardrails and internal administration APIs add complexity without corresponding benefit over standard category filters.
C. Instruction injection, DynamoDB storage, Step Functions validation, and Amazon Comprehend post-processing create significant custom maintenance overhead and orchestration complexity.
D. Custom templates in DynamoDB, multiple Lambda functions, and post-processing with Comprehend introduce unnecessary operational overhead compared to native Bedrock Prompt Management and Guardrails.

Question 5

A financial services company is building a customer support application that retrieves relevant financial regulation documents from a database based on semantic similarities to user queries. The application must integrate with Amazon Bedrock to generate responses. The application must be able to search documents that are in English, Spanish, and Portuguese. The application must filter documents by metadata such as publication date, regulatory agency, and document type. The database stores approximately 10 million document embeddings. To minimize operational overhead, the company wants a solution that minimizes management and maintenance effort. The application must provide low-latency responses for real-time customer interactions. Which solution will meet these requirements?

A. Use Amazon OpenSearch Serverless to provide vector search capabilities and metadata filtering. Connect to Amazon Bedrock Knowledge Bases to enable Retrieval Augmented Generation (RAG) capabilities that use an Anthropic Claude foundation model (FM).
B. Deploy an Amazon Aurora PostgreSQL database with the pgvector extension. Define tables to store embeddings and metadata. Use SQL queries to perform similarity searches. Send retrieved documents to Amazon Bedrock to generate responses.
C. Use Amazon S3 Vectors to configure a vector index and non-filterable metadata fields. Integrate S3 Vectors with Amazon Bedrock to enable Retrieval Augmented Generation (RAG) capabilities.
D. Set up an Amazon Neptune Analytics graph database. Configure a vector index that has appropriate dimensionality to store document embeddings. Use Amazon Bedrock to perform graph-based retrieval and to generate responses.

Show answer and explanation ▾

Correct answer: A

Amazon OpenSearch Serverless provides vector search capabilities with built-in metadata filtering for publication date, regulatory agency, and document type across 10 million embeddings. It supports multi-language semantic search (English, Spanish, Portuguese) natively and integrates seamlessly with Amazon Bedrock Knowledge Bases for RAG capabilities using Anthropic Claude models. Serverless architecture minimizes operational overhead while providing low-latency retrieval for real-time customer interactions.

Why the other options are wrong:

B. Aurora PostgreSQL with pgvector requires manual database provisioning, scaling, and operational management, increasing overhead compared to serverless offerings.
C. S3 Vectors does not support metadata filtering, a key requirement for filtering by publication date, regulatory agency, and document type.
D. Neptune Analytics is a graph database designed for relationship queries, not optimized for vector similarity search or the operational simplicity required for this use case.

Question 6

A medical company is building a generative AI (GenAI) application that uses RAG to provide evidence-based medical information. The application uses Amazon OpenSearch Service to retrieve vector embeddings. Users report that searches frequently miss results that contain exact medical terms and acronyms and return too many semantically similar but irrelevant documents. The company needs to improve retrieval quality and maintain low end user latency, even as the document collection grows to millions of documents. Which solution will meet these requirements with the LEAST operational overhead?

A. Configure hybrid search by combining vector similarity with keyword matching to improve semantic understanding and exact term and acronym matching.
B. Increase the dimensions of the vector embeddings from 384 to 1536. Use a post- processing AWS Lambda function to filter out irrelevant results after retrieval.
C. Replace OpenSearch Service with Amazon Kendra. Use query expansion to handle medical acronyms and terminology variants during preprocessing.
D. Implement a two-stage retrieval architecture in which initial vector search results are re-ranked by an ML model that is hosted on Amazon SageMaker AI.

Show answer and explanation ▾

Correct answer: A

Hybrid search combines vector similarity with keyword matching, allowing exact matches on medical terms and acronyms while maintaining semantic understanding. This approach directly addresses both problems: missing results containing exact medical terminology and excessive semantically similar irrelevant documents. Implementing hybrid search in Amazon OpenSearch Service requires minimal operational overhead (configuration change) while maintaining low latency and supporting growth to millions of documents.

Why the other options are wrong:

B. Increasing vector dimensions and post-processing filtering doesn't address the underlying issue that pure semantic search misses exact term matches and requires Lambda post-processing overhead.
C. Replacing OpenSearch with Amazon Kendra introduces significant migration effort and operational changes; Kendra is designed for enterprise document search, not specialized RAG tasks.
D. Two-stage retrieval with SageMaker ML re-ranking adds operational complexity, management overhead, and additional latency, contradicting the low-overhead requirement.

Question 7

A company runs a generative AI (GenAI)-powered summarization application in an application AWS account that uses Amazon Bedrock. The application architecture includes an Amazon API Gateway REST API that forwards requests to AWS Lambda functions that are attached to private VPC subnets. The application summarizes sensitive customer records that the company stores in a governed data lake in a centralized data storage account. The company has enabled Amazon S3, Amazon Athena, and AWS Glue in the data storage account. The company must ensure that calls that the application makes to Amazon Bedrock use only private connectivity between the company's application VPC and Amazon Bedrock. The company's data lake must provide fine-grained column-level access across the company's AWS accounts. Which solution will meet these requirements?

A. In the application account, create interface VPC endpoints for Amazon Bedrock runtimes. Run Lambda functions in private subnets. Use IAM conditions on inference and data-plane policies to allow calls only to approved endpoints and roles. In the data storage account, use AWS Lake Formation LF-tag-based access control to create table and column-level cross-account grants.
B. Run Lambda functions in private subnets. Configure a NAT gateway to provide access to Amazon Bedrock and the data lake. Use S3 bucket policies and ACLs to manage permissions. Export AWS CloudTrail logs to Amazon S3 to perform weekly reviews.
C. Create a gateway endpoint only for Amazon S3 in the application account. Invoke Amazon Bedrock through public endpoints. Use database-level grants in AWS Lake Formation to manage data access. Stream AWS CloudTrail logs to Amazon CloudWatch Logs. Do not set up metric filters or alarms.
D. Use VPC endpoints to provide access to Amazon Bedrock and Amazon S3 in the application account. Use only IAM path-based policies to manage data lake access. Send AWS CloudTrail logs to Amazon CloudWatch Logs. Periodically create dashboards and allow public fallback for cross-Region reads to reduce setup time.

Show answer and explanation ▾

Correct answer: A

Option A creates interface VPC endpoints for Amazon Bedrock runtime in the application account, ensuring private connectivity without internet exposure. Lambda functions in private subnets use these endpoints exclusively with IAM conditions restricting access to approved endpoints and roles. AWS Lake Formation LF-tag-based access control provides fine-grained column-level cross-account access to the data lake, addressing both the private connectivity requirement for Bedrock and the governed data lake access requirement.

Why the other options are wrong:

B. NAT gateways route traffic through public internet, violating the private connectivity requirement; S3 bucket policies and ACLs lack fine-grained column-level control.
C. Invoking Bedrock through public endpoints violates the private connectivity requirement; database-level grants lack column-level granularity required for fine- grained access.
D. Public fallback for cross-Region reads bypasses the private connectivity requirement; IAM path-based policies alone cannot enforce column-level access control like Lake Formation provides.

Question 8

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 to create dashboards that display prompt usage metrics. Store the approval status of content 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 IAM policies to control approval permissions. Create parameterized prompt templates by specifying variables.
C. Use AWS Step Functions to create an approval workflow. Store prompts as documents in Amazon S3. Use tags to implement version control. Use Amazon EventBridge to send notifications.
D. Deploy Amazon SageMaker Canvas with prompt templates that are stored in Amazon S3. Use AWS CloudFormation to implement version control. Use AWS Config to enforce approval policies.

Show answer and explanation ▾

Correct answer: B

Amazon Bedrock Prompt Management is the native service designed specifically for managing prompt templates with version control and approval workflows. CloudTrail provides comprehensive audit logging of all prompt activities. IAM policies control who can approve prompts, and parameterized templates with variables enforce consistent quality standards across teams and regions. This is the purpose-built solution for the company's governance requirements.

Why the other options are wrong:

A. Bedrock Studio is for interactive development, not enterprise prompt management; CloudWatch metrics don't provide approval workflows; DynamoDB with Lambda is a custom solution lacking built-in version control.
C. Step Functions and S3 lack native prompt versioning and approval features; tags are not version control; this requires custom development.
D. SageMaker Canvas is for different use cases; CloudFormation manages infrastructure not prompts; AWS Config doesn't enforce approval workflows.

Question 9

A company is developing a customer support application that uses Amazon Bedrock foundation models (FMs) to provide real-time AI assistance to the company's employees. The application must display AI-generated responses character by character as the responses are generated. The application needs to support thousands of concurrent users with minimal latency. The responses typically take 15 to 45 seconds to finish. Which solution will meet these requirements?

A. Configure an Amazon API Gateway WebSocket API with an AWS Lambda integration. Configure the WebSocket API to invoke the Amazon Bedrock InvokeModelWithResponseStream API and stream partial responses through WebSocket connections.
B. Configure an Amazon API Gateway REST API with an AWS Lambda integration. Configure the REST API to invoke the Amazon Bedrock standard InvokeModel API and implement frontend client-side polling every 100 ms for complete response chunks.
C. Implement direct frontend client connections to Amazon Bedrock by using IAM user credentials and the InvokeModelWithResponseStream API without any intermediate gateway or proxy layer.
D. Configure an Amazon API Gateway HTTP API with an AWS Lambda integration. Configure the HTTP API to cache complete responses in an Amazon DynamoDB table and serve the responses through multiple paginated GET requests to frontend clients.

Show answer and explanation ▾

Correct answer: A

WebSocket APIs enable persistent bidirectional connections ideal for streaming character- by-character output. The InvokeModelWithResponseStream API is specifically designed for streaming partial model outputs. Lambda can handle thousands of concurrent WebSocket connections efficiently. This architecture supports real-time display of AI responses with minimal latency and scales to thousands of concurrent users.

Why the other options are wrong:

B. REST APIs with polling every 100ms creates unnecessary latency and load; standard InvokeModel is not designed for streaming; polling is inefficient for long 15-45 second responses.
C. Direct client connections to Bedrock expose AWS credentials and lack proper API management; this violates security best practices and doesn't scale well.
D. HTTP API with caching and pagination defeats the purpose of real-time streaming; DynamoDB persistence adds latency; paginated GET requests don't provide character- by-character streaming experience.

Question 10

A healthcare company is using Amazon Bedrock to build a Retrieval Augmented Generation (RAG) application that helps practitioners make clinical decisions. The application must achieve high accuracy for patient information retrievals, identify hallucinations in generated content, and reduce human review costs. Which solution will meet these requirements?

A. Use Amazon Comprehend to analyze and classify RAG responses and to extract medical entities and relationships. Use AWS Step Functions to orchestrate automated evaluations. Configure Amazon CloudWatch metrics to track entity recognition confidence scores. Configure CloudWatch to send an alert when accuracy falls below specified thresholds.
B. Implement automated large language model (LLM)-based evaluations that use a specialized model that is fine-tuned for medical content to assess all responses. Deploy AWS Lambda functions to parallelize evaluations. Publish results to Amazon CloudWatch metrics that track relevance and factual accuracy.
C. Configure Amazon CloudWatch Synthetics to generate test queries that have known answers on a regular schedule, and track model success rates. Set up dashboards that compare synthetic test results against expected outcomes.
D. Deploy a hybrid evaluation system that uses an automated LLM-as-a-judge evaluation to initially screen responses and targeted human reviews for edge cases. Use Amazon SageMaker Feature Store to maintain evaluation datasets. Use a built- in Amazon Bedrock evaluation to track retrieval precision and hallucination rates.

Show answer and explanation ▾

Correct answer: D

Deploy a hybrid evaluation system that uses an automated LLM-as-a-judge evaluation to initially screen responses and targeted human reviews for edge cases. Use Amazon SageMaker Feature Store to maintain evaluation datasets. Use a built-in Amazon Bedrock evaluation to track retrieval precision and hallucination rates. A hybrid evaluation system combining automated LLM-as-a-judge screening with targeted human review for edge cases provides high accuracy while reducing review costs. Amazon SageMaker Feature Store maintains evaluation datasets for consistency. Built-in Amazon Bedrock evaluation tracking for retrieval precision and hallucination rates directly addresses the requirement to identify hallucinations. This balanced approach meets accuracy, hallucination detection, and cost reduction requirements.

Why the other options are wrong:

A. Amazon Comprehend is for general NLP tasks, not specialized medical hallucination detection; CloudWatch alerts track symptoms but don't actively prevent hallucinations.
B. Fine-tuned medical models require significant development effort; Lambda parallelization doesn't inherently reduce hallucinations or identify retrieval accuracy issues.
C. Synthetics generate synthetic test data but don't evaluate actual patient information retrieval quality; this doesn't address hallucination detection in real clinical contexts.

Question 11

A financial services company is developing a customer service AI assistant by using Amazon Bedrock. The AI assistant must not discuss investment advice with users. The AI assistant must block harmful content, mask personally identifiable information (PII), and maintain audit trails for compliance reporting. The AI assistant must apply content filtering to both user inputs and model responses based on content sensitivity. The company requires an Amazon Bedrock guardrail configuration that will effectively enforce policies with minimal false positives. The solution must provide multiple handling strategies for multiple types of sensitive content. Which solution will meet these requirements?

A. Configure a single guardrail and set content filters to high for all categories. Set up denied topics for investment advice and include sample phrases to block. Set up sensitive information filters that apply the block action for all PII entities. Apply the guardrail to all model inference calls.
B. Configure multiple guardrails by using tiered policies. Create one guardrail and set content filters to high. Configure the guardrail to block PII for public interactions. Configure a second guardrail and set content filters to medium. Configure the second guardrail to mask PII for internal use. Configure multiple topic-specific guardrails to block investment advice and set up contextual grounding checks.
C. Configure a guardrail and set content filters to medium for harmful content. Set up denied topics for investment advice and include clear definitions and sample phrases to block. Configure sensitive information filters to mask PII in responses and to block financial information in inputs. Enable both input and output evaluations that use custom blocked messages for audits.
D. Create a separate guardrail for each use case. Create one guardrail that applies a harmful content filter. Create a guardrail to apply topic filters for investment advice. Create a guardrail to apply sensitive information filters to block PII. Use AWS Step Functions to chain the guardrails together sequentially. Use conditional logic based on content classification.

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Correct answer: C

Option C configures a single guardrail with medium-level content filters (reducing false positives), defines denied topics for investment advice with clear samples, and critically sets sensitive information filters to mask PII in responses while blocking it in inputs- providing different handling strategies for different content types. Enabling both input and output evaluations with custom blocked messages provides audit trails for compliance. This balanced approach meets all requirements with minimal false positives.

Why the other options are wrong:

A. High severity for all categories causes excessive false positives; blocking all PII is inflexible; single guardrail lacks tiered strategies for different content types.
B. Multiple guardrails are more complex and costly; doesn't demonstrate single guardrail can handle multiple requirements; Kendra grounding is not mentioned in requirements.
D. Multiple guardrails with sequential chaining is operationally complex and expensive; Step Functions orchestration adds latency; this doesn't provide the unified control needed for minimal false positives.

Question 12

An ecommerce company is developing a generative AI (GenAI) solution that uses Amazon Bedrock with Anthropic Claude to recommend products to customers. Customers report that some of the recommended products are not available for sale on the website or are not relevant to the customer. Customers also report that the solutions takes a long time to generate some recommendations. The company investigates the issues and finds that most interactions between customers and the product recommendation solution are unique. The company confirms that the solutions recommends products that are not in the company's product catalog. The company must resolve these issues. Which solution will meet this requirement?

A. Increase grounding within Amazon Bedrock Guardrails. Enable Automated Reasoning checks. Set up provisioned throughput.
B. Use prompt engineering to restrict the model responses to relevant products. Use streaming techniques such as the InvokeModelWithResponseStream action to reduce perceived latency for the customers.
C. Create an Amazon Bedrock knowledge base. Implement Retrieval Augmented Generation (RAG). Set the PerformanceConfigLatency parameter to optimized.
D. Store product catalog data in Amazon OpenSearch Service. Validate the model's product recommendations against the product catalog. Use Amazon DynamoDB to implement response caching.

Show answer and explanation ▾

Correct answer: C

Amazon Bedrock knowledge bases with RAG ground the model's responses in the actual product catalog, preventing recommendations of unavailable or irrelevant products. RAG retrieves relevant products from the company's data before generation, ensuring accuracy. The PerformanceConfigLatency parameter set to optimized reduces response generation time. This directly solves both the accuracy problem (wrong products) and latency problem through grounding in reliable product data.

Why the other options are wrong:

A. Guardrails enforce policies but don't ground responses in product catalog data; Automated Reasoning doesn't solve product accuracy; provisioned throughput doesn't address which products are recommended.
B. Prompt engineering alone cannot reliably prevent hallucinations about non-existent products; streaming reduces perceived latency but not actual generation issues; doesn't ground in product catalog.
D. OpenSearch validation is a post-hoc check that doesn't prevent hallucinations; DynamoDB caching helps latency but doesn't improve recommendation accuracy; doesn't ground model in current catalog.

Question 13

A financial services company is developing a Retrieval Augmented Generation (RAG) application to help investment analysts query complex financial relationships across multiple investment vehicles, market sectors, and regulatory environments. The dataset contains highly interconnected entities that have multi-hop relationships. The analysts must be able to examine the relationships holistically to provide accurate investment guidance. The application must deliver comprehensive answers that capture indirect relationships between financial entities. The application must produce responses in less than 3 seconds. Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon Bedrock Knowledge Bases with Graph RAG and Amazon Neptune Analytics to store the financial data. Analyze the multi-hop relationships between entities and automatically identify related information across documents.
B. Use Amazon Bedrock Knowledge Bases and an Amazon OpenSearch Service vector store to implement custom relationship identification logic that uses AWS Lambda functions to query multiple vector embeddings in sequence.
C. Use an Amazon OpenSearch Serverless vector database with k-nearest neighbor (k-NN) searches. Implement manual relationship mapping in an application layer that runs in an Amazon EC2 Auto Scaling group.
D. Use Amazon DynamoDB to store financial data in a custom indexing system. Use an AWS Lambda function to query relevant records based on input questions. Use Amazon SageMaker AI to generate responses.

Show answer and explanation ▾

Correct answer: A

Amazon Bedrock Knowledge Bases combined with Amazon Neptune Analytics provides purpose-built graph database capabilities specifically designed for multi-hop relationship analysis across interconnected financial entities. Neptune Analytics automatically identifies and traverses complex relationships between investment vehicles, sectors, and regulatory frameworks without requiring custom relationship mapping logic. This approach delivers sub-3-second query performance through optimized graph traversal while minimizing operational overhead compared to manual relationship identification or Lambda-based sequential querying. The serverless nature of Bedrock Knowledge Bases reduces infrastructure management burden.

Why the other options are wrong:

B. Requires custom Lambda functions for sequential vector embedding queries, introducing higher operational complexity and likely exceeding 3-second latency for multi-hop queries.
C. Manual relationship mapping in the application layer requires significant custom development and maintenance effort; EC2 Auto Scaling adds operational overhead.
D. DynamoDB with custom indexing and SageMaker for response generation introduces excessive operational complexity and does not efficiently handle multi-hop relationship traversal.

Question 14

A healthcare company uses Amazon Bedrock to deploy an application that generates summaries of clinical documents. The application experiences inconsistent response quality with occasional factual hallucinations. Monthly costs exceed the company's projections by 40%. A GenAI developer must implement a near real-time monitoring solution to detect hallucinations, identify abnormal token consumption, and provide early warnings of cost anomalies. The solution must require minimal custom development work and maintenance overhead. Which solution will meet these requirements?

A. Configure Amazon CloudWatch alarms to monitor InputTokenCount and OutputTokenCount metrics to detect anomalies. Store model invocation logs in an Amazon S3 bucket. Use AWS Glue and Amazon Athena to identify potential hallucinations.
B. Run Amazon Bedrock evaluation jobs that use LLM-based judgments to detect hallucinations. Configure Amazon CloudWatch to track token usage. Create an AWS Lambda function to process CloudWatch metrics. Configure the Lambda function to send usage pattern notifications.
C. Configure Amazon Bedrock to store model invocation logs in an Amazon S3 bucket. Enable text output logging. Configure Amazon Bedrock guardrails to run contextual grounding checks to detect hallucinations. Create Amazon CloudWatch anomaly detection alarms for token usage metrics.
D. Use AWS CloudTrail to log all Amazon Bedrock API calls. Create a custom dashboard in Amazon QuickSight to visualize token usage patterns. Use Amazon SageMaker Model Monitor to detect quality drift in generated summaries.

Show answer and explanation ▾

Correct answer: C

Amazon Bedrock guardrails with contextual grounding checks provide built-in hallucination detection without custom development, directly addressing the quality inconsistency issue. CloudWatch anomaly detection alarms on token usage metrics automatically identify cost anomalies without requiring Lambda functions or complex data pipeline orchestration. Text output logging to S3 with guardrail enforcement enables near real-time detection of factual errors. This solution requires minimal custom development and maintenance while providing comprehensive monitoring for both quality and cost issues.

Why the other options are wrong:

A. Requires AWS Glue and Athena for post-hoc analysis, introducing processing delays that conflict with near real-time requirements and increasing maintenance overhead.
B. Bedrock evaluation jobs are periodic and not near real-time; requires custom Lambda function development for notification logic.
D. CloudTrail logging and custom QuickSight dashboards introduce significant custom development effort; SageMaker Model Monitor requires model-specific configuration and training.

Question 15

A company is building a generative AI (GenAI) application that produces content based on a variety of internal and external data sources. The company wants to ensure that the generated output is fully traceable. The application must support data source registration and enable metadata tagging to attribute content to its original source. The application must also maintain audit logs of data access and usage throughout the pipeline. Which solution will meet these requirements?

A. Use AWS Lake Formation to catalog data sources and control access. Apply metadata tags directly in Amazon S3. Use AWS CloudTrail to monitor API activity.
B. Use AWS Glue Data Catalog to register and tag data sources. Use Amazon CloudWatch Logs to monitor access patterns and application behavior.
C. Store data in Amazon S3 and use object tagging for attribution. Use AWS Glue Data Catalog to manage schema information. Use AWS CloudTrail to log access to S3 buckets.
D. Use AWS Glue Data Catalog to register all data sources. Apply metadata tags to attribute data sources. Use AWS CloudTrail to log access and activity across services.

Show answer and explanation ▾

Correct answer: D

AWS Glue Data Catalog is the primary service for registering and managing data sources with comprehensive metadata tagging capabilities, enabling source attribution throughout the data pipeline. CloudTrail provides the complete audit trail of access and activity across all services, creating a comprehensive lineage record from source through generation. This combination provides full traceability with metadata tags directly linked to registered sources and complete audit logging across the entire pipeline, meeting all traceability requirements.

Why the other options are wrong:

A. Lake Formation is for access control rather than data lineage; lacks built-in attribution and tagging capabilities for source identification.
B. CloudWatch Logs monitors behavior but does not provide data source registration or comprehensive audit trails; lacks metadata tagging for attribution.
C. S3 object tagging is insufficient for comprehensive data lineage; lacks centralized source registration and does not provide the complete audit trail that CloudTrail offers.

Question 16

A financial services company needs to build a document analysis system that uses Amazon Bedrock to process quarterly reports. The system must analyze financial data, perform sentiment analysis, and validate compliance across batches of reports. Each batch contains 5 reports. Each report requires multiple foundation model (FM) calls. The solution must finish the analysis within 10 seconds for each batch. Current sequential processing takes 45 seconds for each batch. Which solution will meet these requirements?

A. Use AWS Lambda functions with provisioned concurrency to process each analysis type sequentially. Configure the Lambda function timeouts to 10 seconds. Configure automatic retries with exponential backoff.
B. Use AWS Step Functions with a Parallel state to invoke separate AWS Lambda functions for each analysis type simultaneously. Configure Amazon Bedrock client timeouts. Use Amazon CloudWatch metrics to track execution time and model inference latency.
C. Create an Amazon SQS queue to buffer analysis requests. Deploy multiple AWS Lambda functions with reserved concurrency. Configure each Lambda function to process different aspects of each report sequentially and then combine the results.
D. Deploy an Amazon ECS cluster that runs containers that process each report sequentially. Use a load balancer to distribute batch workloads. Configure an auto- scaling policy based on CPU utilization to handle demand fluctuations.

Show answer and explanation ▾

Correct answer: B

AWS Step Functions with a Parallel state enables simultaneous execution of multiple analysis types (financial data analysis, sentiment analysis, compliance validation) across separate Lambda functions, reducing execution from 45 seconds to well under 10 seconds by eliminating sequential processing bottleneck. This native orchestration of concurrent Lambda invocations for Bedrock calls is the most direct way to parallelize independent analysis tasks. CloudWatch metrics tracking ensures performance monitoring without introducing additional latency.

Why the other options are wrong:

A. Sequential processing with provisioned concurrency retains the bottleneck; 10- second timeout is too aggressive for batch processing and exponential backoff increases latency.
C. SQS buffering introduces queuing delays; processing different aspects sequentially within Lambda functions maintains the sequential bottleneck.
D. ECS clusters with load balancing and CPU-based scaling add unnecessary operational overhead and do not guarantee sub-10-second execution.

Question 17

A company is using Amazon Bedrock to build a customer-facing AI assistant to handle sensitive customer inquiries. The company must use defense-in-depth safety controls to block sophisticated prompt injection attacks. The company must keep audit logs of all safety interventions. The AI assistant must have cross-Region failover capabilities. Which solution will meet these requirements?

A. Configure Amazon Bedrock guardrails to use content filters to protect against prompt injection attacks. Set the content filters to high. Use a guardrail profile to implement cross-Region guardrail inference. Use Amazon CloudWatch Logs with custom metrics to capture detailed guardrail intervention events.
B. Configure Amazon Bedrock guardrails to use content filters to protect against prompt injection attacks. Set the content filters to high. Use AWS WAF to block suspicious inputs. Use AWS CloudTrail to log API calls for audits.
C. Deploy Amazon Comprehend custom classification to detect prompt injection attacks. Use Amazon API Gateway to validate requests. Use Amazon CloudWatch Logs with custom metrics to capture detailed intervention events.
D. Configure Amazon Bedrock guardrails to use custom content filters to protect against harmful content. Set the content filters to high. Use word filters to protect against known attack patterns. Configure cross-Region guardrail replication to provide failover capabilities. Store logs in AWS CloudTrail for compliance auditing.

Show answer and explanation ▾

Correct answer: A

Amazon Bedrock guardrails with high-level content filters provide defense-in-depth protection against prompt injection attacks with sophisticated filtering logic. Guardrail profiles enable cross-Region inference, providing the required failover capability across regions. CloudWatch Logs with custom metrics capture detailed guardrail intervention events for audit logging. This approach combines specialized guardrail technology with proper audit trails and cross-Region failover in a cohesive solution.

Why the other options are wrong:

B. AWS WAF operates at network layer and cannot detect sophisticated prompt injection within model inputs; CloudTrail logs API calls but not detailed guardrail interventions.
C. Amazon Comprehend custom classification requires custom training and maintenance; API Gateway validation adds latency; does not provide cross-Region failover.
D. Word filters for attack patterns are brittle and easily bypassed; custom content filters require ongoing maintenance; guardrail replication across regions is not a standard feature.

Question 18

A company is designing a canary deployment strategy for a payment processing API. The system must support automated gradual traffic shifting between multiple Amazon Bedrock models based on real-time inference metrics, historical traffic patterns, and service health. The solution must be able to gradually increase traffic to new model versions. The system must increase traffic if metrics remain healthy and decrease traffic if the performance degrades below acceptable thresholds. The company needs to comprehensively monitor inference latency and error rates during the deployment phase. The company must also be able to halt deployments and revert to a previous model version without any manual intervention. Which solution will meet these requirements?

A. Use Amazon Bedrock with provisioned throughput to host the versions of the model. Configure an Amazon EventBridge rule to invoke an AWS Step Functions workflow when a new model version is released. Configure the workflow to shift traffic in stages, wait for a specified time period, and invoke an AWS Lambda function to check Amazon CloudWatch performance metrics. Configure the workflow to increase traffic if the metrics meet thresholds and to trigger a traffic rollback if performance metrics fall below thresholds.
B. Use AWS Lambda functions to invoke various Amazon Bedrock model versions. Use an Amazon API Gateway HTTP API with stage variables and weighted routing to shift traffic gradually to new model versions. Use Amazon CloudWatch to monitor performance metrics. Use external logic to adjust traffic between model versions and to roll back if performance falls below thresholds.
C. Use Amazon SageMaker AI endpoint variants to represent multiple Amazon Bedrock model versions. Use variant weights to shift traffic. Use Amazon CloudWatch to monitor performance metrics. Use SageMaker Model Monitor to trigger AWS Lambda functions to roll back a model deployment if performance drops below a specified threshold. Configure an Amazon EventBridge rule to roll back model deployments if an anomaly is detected.
D. Use Amazon OpenSearch Service to track inference logs. Configure OpenSearch Service to invoke an AWS Systems Manager Automation runbook to update Amazon Bedrock model endpoints to shift traffic based on the inference logs.

Show answer and explanation ▾

Correct answer: A

AWS Step Functions with EventBridge orchestration provides fully automated canary deployment with traffic shifting stages, metric evaluation, and rollback capability without manual intervention. Provisioned throughput ensures consistent performance during gradual traffic shifts. Step Functions' native workflow engine enables checking CloudWatch metrics at each stage and automatically rolling back if performance degrades. This declarative approach to gradual traffic shifting with automated rollback directly implements the canary deployment pattern.

Why the other options are wrong:

B. API Gateway weighted routing requires manual adjustment of weights; 'external logic' implies manual intervention, not automated deployment.
C. SageMaker endpoints are designed for ML inference, not for managing Bedrock model versions; Model Monitor triggers Lambda functions but requires manual orchestration.
D. OpenSearch Service tracking combined with Systems Manager Automation runbooks introduces unnecessary complexity; does not provide integrated traffic shifting capabilities.

Question 19

A financial services company uses an AI application to process financial documents by using Amazon Bedrock. During business hours, the application handles approximately 10,000 requests each hour, which requires consistent throughput. The company uses the CreateProvisionedModelThroughput API to purchase provisioned throughput. Amazon CloudWatch metrics show that the provisioned capacity is unused while on-demand requests are being throttled. The company finds the following code in the application: python response = bedrock_runtime.invoke_model(modelId="anthropic.claude- v2", body=json.dumps(payload)) The company needs the application to use the provisioned throughput and to resolve the throttling issues. Which solution will meet these requirements?

A. Increase the number of model units (MUs) in the provisioned throughput configuration.
B. Replace the model ID parameter with the ARN of the provisioned model that the CreateProvisionedModelThroughput API returns.
C. Add exponential backoff retry logic to handle throttling exceptions during peak hours.
D. Modify the application to use the InvokeModelWithResponseStream API instead of the InvokeModel API.

Show answer and explanation ▾

Correct answer: B

The application code uses the string model ID 'anthropic.claude-v2' instead of the ARN returned by CreateProvisionedModelThroughput API, causing Bedrock to route requests to on-demand capacity instead of provisioned throughput. Replacing the model ID with the provisioned model ARN directs all requests to the allocated provisioned capacity, resolving throttling and utilizing the purchased throughput. This is the specific configuration required by Bedrock's API.

Why the other options are wrong:

A. Increasing MUs does not resolve the issue if requests are not being routed to provisioned capacity; the problem is routing, not capacity.
C. Exponential backoff handles throttling exceptions but does not redirect traffic to provisioned throughput; merely masks the underlying routing issue.
D. InvokeModelWithResponseStream is for streaming responses but does not affect which capacity tier (provisioned vs on-demand) handles requests.

Question 20

A company is building an AI advisory application by using Amazon Bedrock. The application will provide recommendations to customers. The company needs the application to explain its reasoning process and cite specific sources for data. The application must retrieve information from company data sources and show step-by-step reasoning for recommendations. The application must also link data claims to source documents and maintain response latency under 3 seconds. Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon Bedrock Knowledge Bases with source attribution enabled. Use the Anthropic Claude Messages API with RAG to set highrelevance thresholds for source documents. Store reasoning and citations in Amazon S3 for auditing purposes.
B. Use Amazon Bedrock with Anthropic Claude models and extended thinking. Configure a 4,000-token thinking budget. Store reasoning traces and citations in Amazon DynamoDB for auditing purposes.
C. Configure Amazon SageMaker AI with a custom Anthropic Claude model. Use the model's reasoning parameter and AWS Lambda to process responses. Add source citations from a separate Amazon RDS database.
D. Use Amazon Bedrock with Anthropic Claude models and chain-of-thought reasoning. Configure custom retrieval tracking with the Amazon Bedrock Knowledge Bases API. Use Amazon CloudWatch to monitor response latency metrics.

Show answer and explanation ▾

Correct answer: A

Amazon Bedrock Knowledge Bases with source attribution enabled provides built-in mechanisms to cite specific sources and explain reasoning within responses. The integration with Anthropic Claude's RAG capabilities automatically handles retrieval and citation with relevance thresholds, maintaining sub-3-second latency while offering least operational overhead. Source attribution is a native Knowledge Bases feature requiring no custom implementation, and S3 storage for auditing is straightforward and scalable.

Why the other options are wrong:

B. Extended thinking with 4,000-token budget increases latency and token costs; requires manual citation management and storage in DynamoDB.
C. Custom SageMaker configuration introduces operational complexity; AWS Lambda processing adds latency; RDS sourcing for citations is indirect.
D. Chain-of-thought reasoning does not inherently provide source citations; custom retrieval tracking requires additional implementation beyond standard Knowledge Bases features.

Question 21

A financial services company uses multiple foundation models (FMs) through Amazon Bedrock for its generative AI (GenAI) applications. To comply with a new regulation for GenAI use with sensitive financial data, the company needs a token management solution. The token management solution must proactively alert when applications approach model-specific token limits. The solution must also process more than 5,000 requests each minute and maintain token usage metrics to allocate costs across business units. Which solution will meet these requirements?

A. Develop model-specific tokenizers in an AWS Lambda function. Configure the Lambda function to estimate token usage before sending requests to Amazon Bedrock. Configure the Lambda function to publish metrics to Amazon CloudWatch and trigger alarms when requests approach thresholds. Store detailed token usage in Amazon DynamoDB to report costs.
B. Implement Amazon Bedrock Guardrails with token quota policies. Capture metrics on rejected requests. Configure Amazon EventBridge rules to trigger notifications based on Amazon Bedrock Guardrails metrics. Use Amazon CloudWatch dashboards to visualize token usage trends across models.
C. Deploy an Amazon SQS dead-letter queue for failed requests. Configure an AWS Lambda function to analyze token-related failures. Use Amazon CloudWatch Logs Insights to generate reports on token usage patterns based on error logs from Amazon Bedrock API responses.
D. Use Amazon API Gateway to create a proxy for all Amazon Bedrock API calls. Configure request throttling based on custom usage plans with predefined token quotas. Configure API Gateway to reject requests that will exceed token limits.

Show answer and explanation ▾

Correct answer: A

Option A directly addresses all requirements: Lambda functions with model-specific tokenizers can estimate token usage before sending requests to Bedrock, CloudWatch metrics and alarms handle proactive alerting for approaching token limits, and DynamoDB stores detailed usage data for cost allocation. Lambda scales efficiently to handle 5,000+ requests per minute. This approach provides complete visibility and control over token consumption for compliance with financial regulations.

Why the other options are wrong:

B. Bedrock Guardrails with token quota policies would reject requests reactively rather than proactively alert when approaching limits, and EventBridge rules lack the granular token estimation capability needed.
C. Using SQS dead-letter queues and analyzing errors post-failure is reactive rather than proactive, and CloudWatch Logs Insights cannot provide accurate token estimation before requests are sent.
D. API Gateway throttling is coarse-grained and cannot perform model-specific token estimation, nor can it effectively track detailed token usage metrics for cost allocation across business units.

Question 22

A retail company is developing a customer service application that must process 10,000 daily queries about products, orders, and warranties. The application must be able to respond to queries about 50,000 product documents that are updated every day. The application must integrate with an order management API to check the status of orders and to help process returns. The application must maintain context throughout multi-turn interactions with customers. The company must collect complete audit trails for application responses. Which solution will meet these requirements with the LEAST operational overhead?

A. Deploy a fine-tuned Amazon Bedrock Anthropic Claude model for each product category. Create AWS Lambda functions to connect each model to the order management API. Store conversation history in Amazon DynamoDB.
B. Create a custom model that uses continued pre-training on Amazon Bedrock to handle all product documentation. Set up an Amazon API Gateway REST API that uses AWS Lambda functions to connect the model to the order management API.
C. Use Amazon SageMaker AI with containers to deploy models. Use Amazon Kendra to search product documents. Use AWS Step Functions to orchestrate calls to the order management API.
D. Use an Amazon Bedrock agent with action groups to integrate with the order management API. Associate an Amazon Bedrock knowledge base with the agent to search product documentation by using Retrieval Augmentation Generation (RAG). Enable trace events to capture audit trails.

Show answer and explanation ▾

Correct answer: D

Option D uses Amazon Bedrock agents with action groups for order management integration and a knowledge base with RAG for product document retrieval, which minimizes operational overhead. Agents automatically handle multi-turn context management, and trace events provide built-in audit trail collection without custom implementation. Knowledge bases handle daily updates of 50,000 documents efficiently through semantic search, eliminating the need to manage multiple fine-tuned models or custom orchestration logic.

Why the other options are wrong:

A. Fine-tuning multiple models per product category creates significant operational overhead and doesn't scale efficiently for 50,000 daily document updates.
B. Custom continued pre-training is operationally intensive and Amazon Kendra requires manual orchestration with Step Functions to coordinate all components.
C. SageMaker with containers, Kendra, and Step Functions introduces more operational complexity than using managed Bedrock agents and knowledge bases.

Question 23

An ecommerce company is using Amazon Bedrock to build a generative AI (GenAI) application. The application uses AWS Step Functions to orchestrate a multi-agent workflow to produce detailed product descriptions. The workflow consists of three sequential states: a description generator, a technical specifications validator, and a brand voice consistency checker. Each state produces intermediate reasoning traces and outputs that are passed to the next state. The application uses an Amazon S3 bucket for process storage and to store outputs. During testing, the company discovers that outputs between Step Functions states frequently exceed the 256 KB quota and cause workflow failures. A GenAI Developer needs to revise the application architecture to efficiently handle the Step Functions 256 KB quota and maintain workflow observability. The revised architecture must preserve the existing multi-agent reasoning and acting (ReAct) pattern. Which solution will meet these requirements with the LEAST operational overhead?

A. Store intermediate outputs in Amazon DynamoDB. Pass only references between states. Create a Map state that retrieves the complete data from DynamoDB when required for each agent's processing step.
B. Configure an Amazon Bedrock integration to use the S3 bucket URI in the input parameter for large outputs. Use the ResultPath field and the ResultSelector field to route S3 references between the agent steps while maintaining the sequential validation workflow.
C. Use AWS Lambda functions to compress outputs to less than 256 KB before each agent state. Configure each agent task to decompress the outputs before processing and to compress results before passing them to the next state.
D. Configure a separate Step Functions state machine to handle each agent's processing. Use Amazon EventBridge to coordinate the execution flow between state machines. Use S3 references for the outputs as event data.

Show answer and explanation ▾

Correct answer: B

Option B directly solves the 256 KB quota issue by using S3 URIs as references between states rather than passing large payloads, while ResultPath and ResultSelector fields maintain sequential workflow integrity. This preserves the ReAct pattern without additional operational overhead like compression/decompression or DynamoDB management. Amazon Bedrock's built-in S3 integration elegantly handles this constraint while keeping multi-agent reasoning intact.

Why the other options are wrong:

A. DynamoDB with a Map state adds unnecessary complexity and operational overhead when S3 references are simpler and more cost-effective for large intermediate outputs.
C. Compression and decompression adds computational overhead at each step and complexity without addressing the architectural issue of payload size limits.
D. Separate state machines with EventBridge coordination significantly increases operational complexity and reduces the observability benefits of a single workflow.

Question 24

A company provides a service that helps users from around the world discover new restaurants. The service has 50 million monthly active users. The company wants to implement a semantic search solution across a database that contains 20 million restaurants and 200 million reviews. The company currently stores the data in a PostgresQL database. The solution must support complex natural language queries and return results for at least 95% of queries within 500 ms. The solution must maintain data freshness for restaurant details that update hourly. The solution must also scale cost-effectively during peak usage periods. Which solution will meet these requirements with the LEAST development effort?

A. Migrate the restaurant data to Amazon OpenSearch Service. Implement keyword- based search rules that use custom analyzers and relevance tuning to find restaurants based on attributes such as cuisine type, feature, and location. Create Amazon API Gateway HTTP API endpoints to transform user queries into structured search parameters.
B. Migrate the restaurant data to Amazon OpenSearch Service. Use a foundation model (FM) in Amazon Bedrock to generate vector embeddings from restaurant descriptions, reviews, and menu items. When users submit natural language queries, convert the queries to embeddings by using the same FM. Perform k-nearest neighbors (k-NN) searches to find semantically similar results.
C. Keep the restaurant data in PostgresQL and implement a pgvector extension. Use a foundation model (FM) in Amazon Bedrock to generate vector embeddings from restaurant data. Store the vector embeddings directly in PostgreSQL. Create an AWS Lambda function to convert natural language queries to vector representations by using the same FM. Configure the Lambda function to perform similarity searches within the database.
D. Migrate restaurant data to an Amazon Bedrock knowledge base by using a custom ingestion pipeline. Configure the knowledge base to automatically generate embeddings from restaurant information. Use the Amazon Bedrock Retrieve API with built-in vector search capabilities to query the knowledge base directly by using natural language input.

Show answer and explanation ▾

Correct answer: B

Option B requires the least development effort for a semantic search solution: OpenSearch Service handles large-scale indexing (20M restaurants, 200M reviews), Foundation Models in Bedrock provide embeddings for natural language queries, and k- NN search within OpenSearch delivers sub-500ms latency at 50M users. OpenSearch handles hourly updates efficiently, scales cost-effectively for peak usage, and all components are managed AWS services requiring minimal custom development.

Why the other options are wrong:

A. Keyword-based search with custom analyzers cannot handle complex natural language queries requiring semantic understanding and won't match the 500ms performance requirement.
C. pgvector in PostgreSQL would not scale cost-effectively for 50 million queries and lacks the distributed architecture needed for 500ms response times at this scale.
D. Amazon Bedrock knowledge bases are designed for document retrieval workflows, not for real-time semantic search across 200 million reviews with the required 500ms latency.

Question 25

A medical company uses Amazon Bedrock to power a clinical documentation summarization system. The system produces inconsistent summaries when handling complex clinical documents. The system performed well on simple clinical documents. The company needs a solution that diagnoses inconsistencies, compares prompt performance against established metrics, and maintains historical records of prompt versions. Which solution will meet these requirements?

A. Create multiple prompt variants by using Prompt management in Amazon Bedrock. Manually test the prompts with simple clinical documents. Deploy the highest performing version by using the Amazon Bedrock console.
B. Implement version control for prompts in a code repository with a test suite that contains complex clinical documents and quantifiable evaluation metrics. Use an automated testing framework to compare prompt versions and document performance patterns.
C. Deploy each new prompt version to separate Amazon Bedrock API endpoints. Split production traffic between the endpoints. Configure Amazon CloudWatch to capture response metrics and user feedback for automatic version selection.
D. Create a custom prompt evaluation flow in Amazon Bedrock Flows that applies the same clinical document inputs to different prompt variants. Use Amazon Comprehend Medical to analyze and score the factual accuracy of each version.

Show answer and explanation ▾

Correct answer: B

Option B provides a systematic approach to diagnosing inconsistencies through version control, quantifiable evaluation metrics on complex documents, and automated testing frameworks to compare prompt performance. This methodology identifies which prompt versions perform better on difficult cases, maintains historical records for compliance, and uses objective metrics to measure improvements-directly addressing hallucination diagnosis and performance comparison requirements.

Why the other options are wrong:

A. Manual testing with simple documents won't diagnose why complex documents produce inconsistencies, and lacks rigorous evaluation metrics or version history.
C. Traffic splitting between endpoints is a deployment strategy, not a diagnostic tool; it cannot systematically compare performance or maintain version history.
D. While Amazon Comprehend Medical could help analyze accuracy, using Flows to test variants doesn't provide version control, historical records, or systematic performance comparison capabilities.

Question 26

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. The 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. Which 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.

Show answer and explanation ▾

Correct answer: C

Option C provides comprehensive observability specifically designed for application resources with Application Insights, creates custom metrics for recommendation quality and token usage, uses CloudWatch anomaly detection to automatically compare against baselines, and CloudWatch Logs Insights for pattern analysis-all alert within 10 minutes. This integrated approach gives operational performance visibility and detects FM behavior deviations within the required timeframe while remaining focused on the application's specific needs.

Why the other options are wrong:

A. Container Insights focuses on infrastructure metrics, not FM behavior; basic CloudWatch alarms lack anomaly detection and won't automatically detect deviations from baselines.
B. X-Ray and CloudTrail provide tracing but not anomaly detection; QuickSight requires manual analysis and doesn't meet the 10-minute alert requirement for automated deviation detection.
D. OpenSearch with Kinesis ingestion adds significant operational overhead and complexity; PPL queries require manual crafting rather than automatic anomaly detection.

Question 27

An enterprise application uses an Amazon Bedrock foundation model (FM) to process and analyze 50 to 200 pages of technical documents. Users are experiencing inconsistent responses and receiving truncated outputs when processing documents that exceed the FM's context window limits. Which solution will resolve this problem?

A. Configure fixed-size chunking at 4,000 tokens for each chunk with 20% overlap. Use application-level logic to link multiple chunks sequentially until the FM's maximum context window of 200,000 tokens is reached before making inference calls.
B. Use hierarchical chunking with parent chunks of 8,000 tokens and child chunks of 2,000 tokens. Use Amazon Bedrock Knowledge Bases built-in retrieval to automatically select relevant parent chunks based on query context. Configure overlap tokens to maintain semantic continuity.
C. Use semantic chunking with a breakpoint percentile threshold of 95% and a buffer size of 3 sentences. Use the Amazon Bedrock RetrieveAndGenerate API call to dynamically select the most relevant chunks based on embedding similarity scores.
D. Create a pre-processing AWS Lambda function that analyzes document token count by using the FM's tokenizer. Configure the lambda function to split documents into equal segments that fit within 80% of the context window. Configure the Lambda function to process each segment independently before aggregating the results.

Show answer and explanation ▾

Correct answer: C

Option C uses semantic chunking with appropriate settings to maintain document coherence, and the RetrieveAndGenerate API dynamically selects the most relevant chunks based on embedding similarity rather than sequential processing. This prevents both truncation and inconsistency by ensuring only the most contextually relevant content is processed together, while the API handles orchestration automatically. Semantic chunking preserves meaning better than fixed-size approaches.

Why the other options are wrong:

A. Fixed-size chunking with sequential linking ignores query relevance and may fail to select important information; attempting to use 200K tokens wastes context window efficiency.
B. Hierarchical chunking with parent/child selection requires manual configuration and doesn't dynamically rank chunks by relevance to specific queries, leading to suboptimal selections.
D. Processing segments independently and aggregating results loses cross-document context and coherence, leading to fragmented summaries rather than comprehensive analysis.

Question 28

A company is developing a generative AI (GenAI) application that analyzes customer service calls in real-time and generates suggested responses for human customer service agents. The application must process 500,000 concurrent calls during peak hours with less than 200 ms end-to-end latency for each suggestion. The company uses existing architecture to transcribe customer call audio streams. The application must not exceed a pre-defined monthly compute budget and must maintain auto scaling capabilities. Which solution will meet these requirements?

A. Deploy a large, complex reasoning model on Amazon Bedrock. Purchase provisioned throughput and optimize for batch processing.
B. Deploy a low-latency, real-time optimized model on Amazon Bedrock. Purchase provisioned throughput and set up automatic scaling policies.
C. Deploy a large language model (LLM) on an Amazon SageMaker AI real-time endpoint that uses dedicated GPU instances.
D. Deploy a mid-sized language model on an Amazon SageMaker AI serverless endpoint that is optimized for batch processing.

Show answer and explanation ▾

Correct answer: B

The requirement for 500,000 concurrent calls with sub-200ms latency demands a low- latency, real-time optimized model rather than a complex reasoning model. Amazon Bedrock with provisioned throughput provides predictable performance and cost control within the budget constraints. Automatic scaling policies enable handling peak loads while maintaining latency requirements. This setup avoids batch processing delays and SageMaker instance cold-start issues that would violate the latency SLA.

Why the other options are wrong:

A. Batch processing is incompatible with real-time suggestion requirements and large complex models add inference latency.
C. SageMaker GPU endpoints require manual scaling management and lack the built-in auto-scaling efficiency of Bedrock provisioned throughput.
D. Serverless endpoints and batch optimization directly conflict with real-time latency and concurrent call requirements.

Question 29

An elevator service company has developed an AI assistant application by using Amazon Bedrock. The application generates elevator maintenance recommendations to support the company's elevator technicians. The company uses Amazon Kinesis Data Streams to collect the elevator sensor data. New regulatory rules require that a human technician must review all AI-generated recommendations. The company needs to establish human oversight workflows to review and approve AI recommendations. The company must store all human technician review decisions for audit purposes. Which solution will meet these requirements?

A. Create a custom approval workflow by using AWS Lambda functions and Amazon SQS queues for human review of AI recommendations. Store all review decisions in Amazon DynamoDB for audit purposes.
B. Create an AWS Step Functions workflow that has a human approval step that uses the waitForTaskToken API to pause execution. After a human technician completes a review, use an AWS Lambda function to call the SendTaskSuccess API that has the approval decision. Store all review decisions in Amazon DynamoDB.
C. Create an AWS Glue workflow that has a human approval step. After the human technician review, integrate the application with an AWS Lambda function that calls the SendTaskSuccess API. Store all human technician review decisions in Amazon DynamoDB.
D. Configure Amazon EventBridge rules with custom event patterns to route AI recommendations to human technicians for review. Create AWS Glue jobs to process human technician approval queues. Use Amazon ElastiCache to cache all human technician review decisions.

Show answer and explanation ▾

Correct answer: B

AWS Step Functions with the waitForTaskToken API is purpose-built for human approval workflows. It pauses execution until a human completes their review, then resumes via SendTaskSuccess with the approval decision. This native workflow orchestration automatically tracks all decisions through Step Functions execution history, providing built- in audit compliance. DynamoDB stores structured approval decisions for additional auditing. Lambda functions handle the callback integration cleanly without requiring custom queue management or polling logic.

Why the other options are wrong:

A. Lambda and SQS require custom polling and queue management logic, making approval tracking less reliable than native Step Functions pause/resume semantics.
C. AWS Glue is a data integration service, not designed for workflow orchestration or human approval tasks; it lacks appropriate pause/resume mechanisms.
D. EventBridge with Glue jobs and ElastiCache is over-engineered for this use case and doesn't provide the structured approval workflow semantics of Step Functions.

Question 30

A software company is using Amazon Q Business to build an AI assistant that allows employees to access company information and personal information by using natural language prompts. The company stores this information in an Amazon S3 bucket. Each department in the company has a dedicated prefix in the S3 bucket. Each object name includes the S3 prefix of the department that it belongs to. Each department can belong to only a single group in AWS IAM Identity Center. Each employee belongs to a single department. The company configures Amazon Q Business to access data stored in an S3 bucket as a data source. The company needs to ensure that the AI assistant respects access controls based on the user's IAM Identity Center group membership. Which solution will meet this requirement with the LEAST operational overhead?

A. Create a JSON file named acl.json in each department folder. In each file, create access control entries that specify the IAM Identity Center group that should have access to that department's data. Indicate the location of the JSON file in the Access Control section of the data source settings.
B. Create a single JSON file named acl.json at the top level of the S3 bucket. Add access control entries that map each department's S3 prefix to its corresponding IAM Identity Center group. Indicate the location of the JSON file in the Access Control section of the data source settings.
C. For each IAM Identity Center group, create a separate permissions set that denies access to all prefixes in the S3 bucket. Add a StringNotEquals condition key to the permissions set for each group that specifies the department each group is associated with. Attach the permissions sets to the Identity Center groups.
D. Create a metadata file named metadata.json at the top level of the S3 bucket. Add an AccessControlList object to the file that specifies the S3 path of each department's prefix. Specify the IAM Identity Center group that should have access to each department's prefix. Reference the file location in the data source metadata settings.

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Correct answer: B

Amazon Q Business natively supports ACL files for access control based on IAM Identity Center group membership. A single centralized acl.json file at the bucket root level with mappings between department S3 prefixes and IAM Identity Center groups provides the least operational overhead. This single point of control avoids maintaining multiple ACL files per department folder and integrates directly with Amazon Q's Access Control section in data source settings. No custom Lambda logic or complex IAM permission sets are required.

Why the other options are wrong:

A. Multiple acl.json files across department folders create maintenance overhead and scaling issues as departments grow.
C. IAM permissions sets with StringNotEquals conditions are overly complex for this use case and don't integrate with Amazon Q's native ACL mechanism.
D. metadata.json with AccessControlList objects is not a recognized Amazon Q access control pattern; the standard approach uses acl.json files.

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