Practice Free AIP-C01 Exam Online Questions
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.
A financial services company is developing a generative AI (GenAI) application that serves both premium customers and standard customers. The application uses AWS Lambda functions behind an Amazon API Gateway REST API to process requests. The company needs to dynamically switch between AI models based on which customer tier each user belongs to. The company also wants to perform A/B testing for new features without redeploying code. The company needs to validate model parameters like temperature and maximum token limits before applying changes.
Which solution will meet these requirements with the LEAST operational overhead?
- A . Create AWS Systems Manager Parameter Store parameters for each configuration. Use Lambda functions to poll for parameter updates. Use Amazon EventBridge events to trigger redeployments when configurations change.
- B . Store model configurations in Amazon DynamoDB tables. Optimize access patterns to retrieve configurations according to customer tier. Configure Lambda functions to query DynamoDB at the beginning of each request to determine which model to use.
- C . Use AWS AppConfig to manage model configurations. Use feature flags to perform A/B testing. Define JSON schema validation rules for model parameters. Configure Lambda functions to retrieve configurations by using the AWS AppConfig Agent.
- D . Create an Amazon ElastiCache (Redis OSS) cluster to store model configurations. Set short TTL values. Run custom validation logic in Lambda functions. Use Amazon CloudWatch metrics to monitor configuration usage.
A financial services company is developing an AI-powered search assistant application to help investment advisors quickly retrieve investment data. The application runs as an AWS Lambda function. The company is using Amazon Bedrock to develop the application by using an Amazon Bedrock knowledge base that uses Amazon OpenSearch Serverless as its data source. The application agent must manage collections at scale by automatically assigning access permissions to collections and indexes that match a specific pattern. The company uses Amazon Bedrock tools to test the knowledge base. The knowledge base sync process finishes successfully. However, the test reveals a 400 Bad Authorization error from the BedrockAgentRuntime API and a 403 Forbidden error when the test attempts to access OpenSearch Serverless. The company must resolve the permissions issues.
Which combination of solutions will meet this requirement? (Select TWO.)
- A . Update the Lambda function execution role to include the bedrock: InvokeAgent permission. Add the aoss: APIAccessAll permission to the Lambda execution role.
- B . Create an OpenSearch Serverless data access policy that includes pattern-based resource rules.
- C . Configure a VPC endpoint policy for OpenSearch Serverless. Add the endpoint to the Lambda function’s VPC configuration.
- D . Configure AWS Secrets Manager to store OpenSearch Serverless credentials. Grant the Lambda function access to retrieve the credentials.
- E . Enable IAM authentication for the OpenSearch Serverless domain. Add the es: ESHttp* permission to the Lambda function execution role.
A company is designing an API for a generative AI (GenAI) application that uses a foundation model (FM) that is hosted on a managed model service. The API must stream responses to reduce latency, enforce token limits to manage compute resource usage, and implement retry logic to handle model timeouts and partial responses.
Which solution will meet these requirements with the LEAST operational overhead?
- A . Integrate an Amazon API Gateway HTTP API with an AWS Lambda function to invoke Amazon Bedrock. Use Lambda response streaming to stream responses. Enforce token limits within the Lambda function. Implement retry logic for model timeouts by using Lambda and API Gateway timeout configurations.
- B . Connect an Amazon API Gateway HTTP API directly to Amazon Bedrock. Simulate streaming by using client-side polling. Enforce token limits on the frontend. Configure retry behavior by using API Gateway integration settings.
- C . Connect an Amazon API Gateway WebSocket API to an Amazon ECS service that hosts a containerized inference server. Stream responses by using the WebSocket protocol. Enforce token limits within Amazon ECS. Handle model timeouts by using ECS task lifecycle hooks and restart policies.
- D . Integrate an Amazon API Gateway REST API with an AWS Lambda function that invokes Amazon Bedrock. Use Lambda response streaming to stream responses. Enforce token limits within the Lambda function. Implement retry logic by using Lambda and API Gateway timeout configurations.
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.
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 with 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.
A company runs a Retrieval Augmented Generation (RAG) application that uses Amazon Bedrock Knowledge Bases to perform regulatory compliance queries. The application uses the RetrieveAndGenerateStream API. The application retrieves relevant documents from a knowledge base that contains more than 50,000 regulatory documents, legal precedents, and policy updates. The RAG application is producing suboptimal responses because the initial retrieval often returns semantically similar but contextually irrelevant documents. The poor responses are causing model hallucinations and incorrect regulatory guidance. The company needs to improve the performance of the RAG application so it returns more relevant documents.
Which solution will meet this requirement with the LEAST operational overhead?
- A . Deploy an Amazon SageMaker endpoint to run a fine-tuned ranking model. Use an Amazon API Gateway REST API to route requests. Configure the application to make requests through the REST API to rerank the results.
- B . Use Amazon Comprehend to classify documents and apply relevance scores. Integrate the RAG application’s reranking process with Amazon Textract to run document analysis. Use Amazon Neptune to perform graph-based relevance calculations.
- C . Implement a retrieval pipeline that uses the Amazon Bedrock Knowledge Bases Retrieve API to perform initial document retrieval. Call the Amazon Bedrock Rerank API to rerank the results. Invoke the InvokeModelWithResponseStream operation to generate responses.
- D . Use the latest Amazon reranker model through the reranking configuration within Amazon Bedrock Knowledge Bases. Use the model to improve document relevance scoring and to reorder results based on contextual assessments.
A research company is developing a GenAI system to produce summaries of technical documents. The company must catalog all data sources in a central location. The company needs a solution that can automatically discover and update data sources. The solution must tag each generated summary with citations as metadata that users can query. The solution must retain tamper-evident, immutable audit logs for every model invocation and store input/output records.
Which solution will meet these requirements?
- A . Use Amazon Comprehend to identify data sources in the documents. Store generated summaries in Amazon S3 and enable S3 Object Lock. Use Amazon CloudWatch metrics to generate reports about application throughput. Do not include logs for each invocation.
- B . Use AWS Glue Data Catalog with crawlers to maintain data sources. Store generated summaries in Amazon S3. Write object tags that include a source ID. Store Amazon Bedrock model invocation logs in Amazon S3. Enable S3 Object Lock on the S3 bucket that stores invocation logs. Use AWS CloudTrail log file integrity validation to provide tamper-evident immutability.
- C . Store application outputs in Amazon DynamoDB. Apply item-level tags that include source attribution. Write application events to Amazon CloudWatch Logs. Use IAM roles to provide audit traceability.
- D . Use AWS AppConfig feature flags to implement data versioning. Restrict access to the model by using IAM condition keys. Maintain a versioned mapping file of source-to-output relationships in Amazon S3.
A company uses AWS Lake Formation to set up a data lake that contains databases and tables for multiple business units across multiple AWS Regions. The company wants to use a foundation model (FM) through Amazon Bedrock to perform fraud detection. The FM must ingest sensitive financial data from the data lake. The data includes some customer personally identifiable information (PII). The company must design an access control solution that prevents PII from appearing in a production environment. The FM must access only authorized data subsets that have PII redacted from specific data columns. The company must capture audit trails for all data access.
Which solution will meet these requirements?
- A . Create a separate dataset in a separate Amazon S3 bucket for each business unit and Region combination. Configure S3 bucket policies to control access based on IAM roles that are assigned to FM training instances. Use S3 access logs to track data access.
- B . Configure the FM to authenticate by using AWS Identity and Access Management roles and Lake Formation permissions based on LF-Tag expressions. Define business units and Regions as LF-Tags that are assigned to databases and tables. Use AWS CloudTrail to collect comprehensive audit trails of data access.
- C . Use direct IAM principal grants on specific databases and tables in Lake Formation. Create a custom application layer that logs access requests and further filters sensitive columns before sending data to the FM.
- D . Configure the FM to request temporary credentials from AWS Security Token Service. Access the data by using presigned S3 URLs that are generated by an API that applies business unit and Regional filters. Use AWS CloudTrail to collect comprehensive audit trails of data access.
A company upgraded its Amazon BedrockCpowered foundation model (FM) that supports a multilingual customer service assistant. After the upgrade, the assistant exhibited inconsistent behavior across languages. The assistant began generating different responses in some languages when presented with identical questions.
The company needs a solution to detect and address similar problems for future updates. The evaluation must be completed within 45 minutes for all supported languages. The evaluation must process at least 15,000 test conversations in parallel. The evaluation process must be fully automated and integrated into the CI/CD pipeline. The solution must block deployment if quality thresholds are not met.
Which solution will meet these requirements?
- A . Create a distributed traffic simulation framework that sends translation-heavy workloads to the assistant in multiple languages simultaneously. Use Amazon CloudWatch metrics to monitor latency, concurrency, and throughput. Run simulations before production releases to identify infrastructure bottlenecks.
- B . Deploy the assistant in multiple AWS Regions with Amazon Route 53 latency-based routing and AWS Global Accelerator to improve global performance. Store multilingual conversation logs in Amazon S3. Perform weekly post-deployment audits to review consistency.
- C . Create a pre-processing pipeline that normalizes all incoming messages into a consistent format before sending the messages to the assistant. Apply rule-based checks to flag potential hallucinations in the outputs. Focus evaluation on normalized text to simplify testing across languages.
- D . Set up standardized multilingual test conversations with identical meaning. Run the test conversations in parallel by using Amazon Bedrock model evaluation jobs. Apply similarity and hallucination thresholds. Integrate the process into the CI/CD pipeline to block releases that fail.