AIP-C01 sample exam questions

15 exam-style questions for AWS AIP-C01: Generative AI Developer Professional, with answers and explanations. Take them as a timed practice exam first if you want a score out of 1000.

  1. Question 1Single choiceFoundation Model Integration, Data Management, and Compliance
    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?
    1. 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.
    2. 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.
    3. 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.
    4. 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

    Correct answer: C

    Why

    AWS AppConfig delivers routing rules and model choices that can change without a deployment, and the Lambda agent caches them for fast reads across many requests. The function then applies the complex business logic per request. Environment variables and stage variables need updates and cannot express these rules, and authorizers are meant for access decisions.

  2. Question 2Single choiceFoundation Model Integration, Data Management, and Compliance
    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?
    1. 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.
    2. 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.
    3. 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.
    4. 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

    Correct answer: A

    Why

    Bedrock Knowledge Bases with GraphRAG on Neptune Analytics automatically builds relationships between entities and uses them during retrieval, which captures multi-hop connections with little management. Vector search alone finds similarity rather than connected relationships, and the other options need custom relationship logic.

  3. Question 3Single choiceAI Safety, Security, and Governance
    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?
    1. 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.
    2. B
      Use AWS Glue Data Catalog to register and tag data sources. Use Amazon CloudWatch Logs to monitor access patterns and application behavior.
    3. 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.
    4. 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

    Correct answer: D

    Why

    AWS Glue Data Catalog registers data sources and supports tags that attribute content to them, and CloudTrail logs access across services. CloudWatch Logs does not audit access by itself, while S3 tags and Lake Formation cover only part of the requirement for internal and external sources.

  4. Question 4Single choiceOperational Efficiency and Optimization
    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?
    1. 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.
    2. 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.
    3. 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.
    4. 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

    Correct answer: B

    Why

    A Step Functions Parallel state runs the analyses at the same time, so total time falls toward the longest branch rather than the sum, and CloudWatch shows timing. The other options still process work sequentially. An SQS queue also adds wait time, and CPU-based ECS scaling does not help calls that wait on a model.

  5. Question 5Single choiceImplementation and Integration
    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?
    1. 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.
    2. 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.
    3. 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.
    4. 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

    Correct answer: B

    Why

    The Step Functions Bedrock integration can read input from and write output to S3 URIs, so states pass small references rather than large payloads. ResultPath and ResultSelector route those references using the existing bucket, and the sequential workflow stays visible. Compression only delays the limit, and DynamoDB or extra state machines add work.

  6. Question 6Single choiceTesting, Validation, and Troubleshooting
    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?
    1. 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.
    2. 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.
    3. 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.
    4. 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

    Correct answer: B

    Why

    A version-controlled repository keeps prompt history, and a test suite with complex documents and quantifiable metrics shows where and why results change. Testing only simple documents misses the problem, splitting production traffic risks clinical output, and Comprehend Medical extracts entities rather than scoring factual accuracy.

  7. Question 7Multiple responseAI Safety, Security, and Governance
    An ecommerce company is building an internal platform to develop generative AI applications by using Amazon Bedrock foundation models (FMs). Developers need to select models based on evaluations that are aligned to ecommerce use cases. The platform must display accuracy metrics for text generation and summarization in dashboards. The company has custom ecommerce datasets to use as standardized evaluation inputs.
    Which combination of steps will meet these requirements with the LEAST operational overhead? (Choose two.)
    1. A
      Import the datasets to an Amazon S3 bucket. Provide appropriate IAM permissions and cross-origin resource sharing (CORS) permissions to give the evaluation jobs access to the datasets.
    2. B
      Import the datasets to an Amazon S3 bucket. Provide appropriate IAM permissions and a VPC endpoint configuration to give the evaluation jobs access to the datasets.
    3. C
      Configure an AWS Lambda function to create model evaluation jobs on a schedule in the Amazon Bedrock console. Provide the URI of the S3 bucket that contains the datasets as an input. Configure the evaluation jobs to measure the real world knowledge (RWK) score for text generation and BERT Score for summarization. Configure a second Lambda function to check the status of the jobs and publish custom logs to Amazon CloudWatch. Create a custom Amazon CloudWatch Logs Insights dashboard.
    4. D
      Use Amazon SageMaker Clarify on a schedule to create model evaluation jobs. Use open source frameworks to create and run standardized evaluations. Publish results to Amazon CloudWatch namespaces. Use the word error rate score for text generation and toxicity for summarization as metrics for accuracy. Configure an AWS Lambda function to check the status of the jobs and publish custom logs to CloudWatch. Create a custom Amazon CloudWatch Logs Insights dashboard.
    5. E
      Run an Amazon SageMaker AI notebook job on a schedule by using the fmevals or ragas framework to run evaluations that use the datasets in the S3 bucket. Write Python code in the notebook that makes direct InvokeModel API calls to the FMs and processes their responses for evaluation. Publish job status and results to Amazon CloudWatch Logs to measure the real world knowledge (RWK) score for text generation and toxicity for summarization as metrics for accuracy. Create a custom CloudWatch Logs Insights dashboard.
    Show answer

    Correct answer: A, C

    Why

    Console-based Bedrock evaluation jobs read datasets from S3 and require IAM access plus CORS on the bucket. Built-in jobs measure real world knowledge for text generation and BERT Score for summarization, which are accuracy metrics. VPC endpoints are not the requirement here, and toxicity or word error rate are not accuracy measures for these tasks.

  8. Question 8Single choiceFoundation Model Integration, Data Management, and Compliance
    A company uses Amazon Bedrock to develop an AI assistant to provide customer support. Analysis shows that 40% of customer queries use varied phrasing or wording to ask the same questions.
    The company wants a solution to reduce redundant model calls. The solution must ensure that semantically equivalent questions receive consistent answers. The solution must ensure low latency.
    Which solution will meet these requirements?
    1. A
      Deploy an Amazon DynamoDB Accelerator (DAX) cluster as an in-memory cache. Specify the query text as the partition key and the model response text as the sort key. Query the cache by using a filter expression with the LIKE operator.
    2. B
      Use Amazon Bedrock to generate embeddings from customer queries. Use Amazon MemoryDB for Valkey to store hash sets of vector embeddings and model responses. Use a RANGE query to find similar queries and their responses.
    3. C
      Deploy Amazon OpenSearch Service that has k-nearest neighbor (k-NN) capabilities to store query-response text pairs. Use an approximate k-NN technique to find similar queries and their responses.
    4. D
      Create a caching solution by using Amazon DynamoDB to create a global secondary index on the normalized query text. Apply stemming to incoming queries. Query the index of cached customer queries.
    Show answer

    Correct answer: B

    Why

    A semantic cache compares embeddings, so differently worded questions can match a stored answer. MemoryDB performs this search in memory with very low latency, and a similarity range finds close matches. DAX and DynamoDB match text rather than meaning, and OpenSearch can do semantic matching but is slower for caching.

  9. Question 9Single choiceAI Safety, Security, and Governance
    A bank is developing a generative AI (GenAI)-powered AI assistant that uses Amazon Bedrock to assist the bank's website users with account inquiries and financial guidance. The bank must ensure that the AI assistant does not reveal any personally identifiable information (PII) in customer interactions.
    The AI assistant must not send PII in prompts to the GenAI model. The AI assistant must not respond to customer requests to provide investment advice. The bank must collect audit logs of all customer interactions, including any images or documents that are transmitted during customer interactions.
    Which solution will meet these requirements with the LEAST operational effort?
    1. A
      Use Amazon Macie to detect and redact PII in user inputs and in the model responses. Apply prompt engineering techniques to force the model to avoid investment advice topics. Use AWS CloudTrail to capture conversation logs.
    2. B
      Use an AWS Lambda function and Amazon Comprehend to detect and redact PII. Use Amazon Comprehend topic modeling to prevent the AI assistant from discussing investment advice topics. Set up custom metrics in Amazon CloudWatch to capture customer conversations.
    3. C
      Configure Amazon Bedrock guardrails to apply a sensitive information policy to detect and filter PII. Set up a topic policy to ensure that the AI assistant avoids investment advice topics. Use the Converse API to log model invocations. Enable delivery and image logging to Amazon S3.
    4. D
      Use regex controls to match patterns for PII. Apply prompt engineering techniques to avoid returning PII or investment advice topics to customers. Enable model invocation logging, delivery logging, and image logging to Amazon S3.
    Show answer

    Correct answer: C

    Why

    Bedrock Guardrails can detect and filter PII and deny investment advice topics with no custom code, and Bedrock model invocation logging can deliver text and images to S3. Macie scans S3 rather than live prompts, Comprehend topic modeling is not a real-time control, and regex with prompt instructions is not reliable.

  10. Question 10Single choiceImplementation and Integration
    A company is building a generative AI (GenAI) application that processes financial reports and provides summaries for analysts. The application must run two compute environments. In one environment, AWS Lambda function must use the Python SDK to analyze reports on demand. In the second environment, Amazon EKS containers must use the JavaScript SDK to batch process multiple reports on a schedule. The application must maintain conversational context throughout multi-tum interactions, use the same foundation model (FM) across environments, and ensure consistent authentication.
    Which solution will meet these requirements?
    1. A
      Use the Amazon Bedrock InvokeModel API with a separate authentication method for each environment. Store conversation states in Amazon DynamoDB. Use custom I/O formatting logic for each programming language.
    2. B
      Use the Amazon Bedrock Converse API directly in both environments with a common authentication mechanism that uses IAM roles. Store conversation states in Amazon ElastiCache. Creating programming language-specific wrappers for model parameters.
    3. C
      Create a centralized Amazon API Gateway REST API endpoint that handles all model interactions by using the InvokeModel API. Store interaction history in application process memory in each Lambda function or EKS container. Use environment variables to configure model parameters.
    4. D
      Use the Amazon Bedrock Converse API and IAM roles for authentication. Pass previous messages in the request messages array to maintain conversational context. Use programming language-specific SDKs to establish consistent API interfaces.
    Show answer

    Correct answer: D

    Why

    The Converse API offers one consistent request format through both the Python and JavaScript SDKs, and IAM roles work in Lambda and EKS. Multi-turn context is passed in the messages array. InvokeModel needs model-specific formats, ElastiCache adds a service that is not needed, and process memory would be lost between runs.

  11. Question 11Single choiceOperational Efficiency and Optimization
    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?
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    Show answer

    Correct answer: B

    Why

    Bedrock Intelligent Prompt Routing automatically sends each request to a suitable model in the same family based on predicted quality and cost, with no routing code. A classifier stage with Lambda takes more effort and adds latency, one mid-sized model is not cost-optimized, and keyword rules are fragile.

  12. Question 12Single choiceTesting, Validation, and Troubleshooting
    A healthcare company is implementing a clinical knowledge base application on Amazon Bed-rock that provides medical information to doctors. During testing, a quality team discovers that the generative AI (GenAI) model occasionally fabricates medical treatment recommendations that do not originate from the approved clinical guidelines.

    The quality team needs to implement a solution to detect the hallucinations before releasing the application. The solution must analyze model responses against verified medical information. The solution must identify semantic inconsistencies when users ask similar questions in different ways.

    Which solution will meet these requirements?
    1. A
      Deploy a real-time monitoring system that uses Amazon CloudWatch Logs Insights to analyze response patterns and to flag factual inaccuracies based on predefined keywords. Implement AWS Lambda functions to compare responses with known answers from an Amazon Dyna-moDB table.
    2. B
      Create a reference dataset with validated question-answer pairs from clinical guidelines. Im-plement output diffing by using Amazon Bedrock Guardrails for response consistency analysis. Use Amazon Bedrock evaluation to detect factual inaccuracies.
    3. C
      Configure Amazon Bedrock Guardrails with custom rules to detect and block potentially hal-lucinated content by identifying specific patterns in the GenAI responses. Use Amazon Sage-Maker Feature Store to maintain a repository of verified clinical guidelines.
    4. D
      Create an Amazon Bedrock automatic model evaluation job with a custom prompt dataset. Set up anomaly detection alarms to identify factual inaccuracies. Implement Amazon SNS notifi-cations when inaccuracies are detected.
    Show answer

    Correct answer: B

    Why

    A reference dataset of validated question-answer pairs from clinical guidelines gives a trusted comparison, and Bedrock evaluation measures factual accuracy before release, including across rephrased questions. Keyword checks and pattern rules cannot detect subtle fabrication, and alarms alone do not measure accuracy. The output diffing with Guardrails wording in B is loose, but this is the closest fit.

  13. Question 13Single choiceImplementation and Integration
    A global company uses an organization in AWS Organizations to manage 25 AWS accounts. The company’s products have 50,000 daily user interactions. The company is using Amazon Bedrock AgentCore to build an AI assistant.

    The AI assistant must access customer data that is stored in multiple AWS accounts across sever-al business units. The AI assistant needs to perform actions on behalf of specific users by query-ing Amazon DynamoDB tables that contain customer information in each business unit's AWS account. Each business unit maintains its own AWS account that hosts its own DynamoDB ta-bles.

    The company must enforce strict data access security boundaries and must collect full audit trails that show which user’s credentials are used to perform each action. The solution must allow the AgentCore application to access resources across multiple accounts while preserving user identity context. The solution must support the company’s plan to expand to more than 40 AWS accounts in the next year.

    Which solution will meet these requirements with the LEAST operational overhead?
    1. A
      Configure the AI assistant to use an outbound credential provider that runs AssumeRole op-erations to dynamically obtain temporary credentials so the AI assistant can access resources across accounts. Create an IAM role in each business unit account that has a trust relationship to the AI assistant’s execution role and permissions to access the DynamoDB tables. Ensure that AssumeRole calls include session tags to identify the end user.
    2. B
      Create a separate AI assistant in each business unit account. Deploy an Amazon API Gateway REST API that runs request routing logic. Create AWS Lambda authorizers to direct user queries to the appropriate AI assistant based on the customer’s business unit identifier from the request context.
    3. C
      Use AWS Resource Access Manager (AWS RAM) to share the DynamoDB tables from each business unit account to a central account where the AI assistant runs. Configure AWS RAM permissions to allow cross-account access to the DynamoDB tables. Grant the AgentCore execu-tion role permissions to access the shared resources directly.
    4. D
      Configure SCPs for the organization to explicitly allow the AI assistant’s execution role prin-cipal to perform dynamodb:Getltem and dynamodb:Query actions across all member accounts. Configure the AI assistant to reference cross-account DynamoDB table ARNs directly in their resource definitions.
    Show answer

    Correct answer: A

    Why

    An AgentCore outbound credential provider can obtain temporary credentials through AssumeRole, one role per account gives clear security boundaries, and session tags record the end user in CloudTrail. Adding accounts only needs another role. A separate assistant per account is much more work, DynamoDB tables are not shared through AWS RAM here, and SCPs do not grant access.

  14. Question 14Single choiceOperational Efficiency and Optimization
    A financial services company is building a fraud detection system by using Amazon Bedrock. The system will monitor activity in multiple stock trading applications that run in the United States and Europe. The system must process 1,000 transactions every second with sub-500 ms response times. The system must also maintain high availability during connectivity disruptions.

    The company must ensure that data for European customers is processed only in AWS Regions that are based in Europe.

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

    Correct answer: A

    Why

    A European geographic inference profile keeps European data in European Regions while still spreading load across them, and on-demand inference needs no capacity management. Route 53 health checks handle failover, and CloudWatch monitors utilization. Provisioned throughput per Region requires custom routing, Bedrock provisioned capacity lacks standard automatic scaling, and streaming is not needed.

  15. Question 15Single choiceTesting, Validation, and Troubleshooting
    A company is implementing a 2-week initiative to incorporate GenAI into several hundred existing applications to provide context-driven natural language help for users. To simplify the process to choose an appropriate model for each application, a GenAI Developer is building a system to automatically compare foundation models (FMs) based on general text generation accuracy. The system runs a job that performs a series of steps.

    First, the system creates a service role and attaches an IAM permissions policy that allows Amazon Bedrock to invoke the FMs and grants access to Amazon S3. Then the system requests and receives access to all the FMs that need to be tested. Then the system creates S3 buckets to store the evaluation results. Then the system creates evaluation configurations by using the TREX built-in dataset. Finally, the system specifies the Accuracy metric and Generation evaluation type in each evaluation configuration.

    When the GenAI developer attempts to run the system, it fails with an AccessDenied exception. The GenAI developer must resolve the issue.

    Which solution will meet this requirement?
    1. A
      Create a trust policy that defines Amazon Bedrock as the service principal. Attach the trust policy to the service role.
    2. B
      Configure cross-origin resource sharing (CORS) permissions that allow GET and DELETE actions on the S3 buckets.
    3. C
      Change the test dataset from TREX to Gigaword in the evaluation configurations.
    4. D
      Change the evaluation type to Summarization and the metric type to Correctness in the evaluation configurations.
    Show answer

    Correct answer: A

    Why

    The service role's permissions are in place, but Bedrock also needs a trust policy naming it as the service principal so it can assume the role, and its absence causes AccessDenied. CORS applies to browser access, and the dataset or metric choices do not affect access. TREX and Accuracy are valid for general text generation.