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Free AWS Certified Generative AI Developer - Professional AIP-C01 Exam Braindumps
AWS Certified Generative AI Developer - Professional AIP-C01 Questions 33–40 (page: 6)

Updated On: 15-Apr-2026
Page 6 of 14
PDF with 97 Questions

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?

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

Answer(s): B

Explanation:

A real-time, low-latency model on Amazon Bedrock is the best fit for sub-200 ms suggestions, and provisioned throughput provides predictable, consistent capacity for extreme concurrency. Bedrock's managed scaling and throughput controls help the company stay within a fixed monthly compute budget while still supporting automatic scaling behavior during peak hours.



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. 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. 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. 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. 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. 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.

Answer(s): A,C

Explanation:

Storing the standardized ecommerce evaluation datasets in Amazon S3 and granting the evaluation jobs permission to read them is the simplest way to supply consistent inputs at scale. Using Amazon Bedrock model evaluation jobs with those S3 datasets provides managed, use-case-aligned evaluations, and publishing the job outputs to CloudWatch for dashboards adds visibility with less ongoing maintenance than running and managing custom evaluation infrastructure.



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?

  1. 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.
  2. 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 DynamoD
  3. 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.
  4. 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.

Answer(s): B

Explanation:

AWS Step Functions supports a built-in human approval pattern by pausing execution with a task token and resuming only when a reviewer submits the decision through SendTaskSuccess, which enforces required human oversight. Persisting the approval decision in Amazon DynamoDB provides a durable audit record of all technician reviews with minimal custom orchestration compared to building an end-to-end workflow from scratch.



A bank is building a generative AI (GenAI) application that uses Amazon Bedrock to assess loan applications by using scanned financial documents. The application must extract structured data from the documents. The application must redact personally identifiable information (PII) before inference. The application must use foundation models (FMs) to generate approvals. The application must route low-confidence document extraction results to human reviewers who are within the same AWS Region as the loan applicant.

The company must ensure that the application complies with strict Regional data residency and auditability requirements. The application must be able to scale to handle 25,000 applications each day and provide 99.9% availability.

Which combination of solutions will meet these requirements? (Choose three.)

  1. Deploy Amazon Textract and Amazon Augmented AI (Amazon A2I) within the same Region to extract relevant data from the scanned documents. Route low-confidence pages to human reviewers.
  2. Use AWS Lambda functions to detect and redact PII from submitted documents before inference. Apply Amazon Bedrock guardrails to prevent inappropriate or unauthorized content in model outputs. Configure Region-specific IAM roles to enforce data residency requirements and to control access to the extracted data.
  3. Use Amazon Kendra and Amazon OpenSearch Service to extract field level values semantically from the uploaded documents before inference.
  4. Store uploaded documents in Amazon S3 and apply object metadata. Configure IAM policies to store original documents within the same Region as each applicant. Enable object tagging for future audits.
  5. Use AWS Glue Data Quality to validate the structured document data. Use AWS Step Functions to orchestrate a review workflow that includes a prompt engineering step that transforms validated data into optimized prompts before invoking Amazon Bedrock to assess loan applications.
  6. Use Amazon SageMaker Clarify to generate fairness and bias reports based on model scoring decisions that Amazon Bedrock makes.

Answer(s): A,B,D

Explanation:

Using Amazon Textract for structured extraction and Amazon Augmented AI in the same Region routes low- confidence results to human reviewers while keeping data residency intact. Redacting PII before invoking Amazon Bedrock ensures sensitive identifiers are removed prior to inference, and applying Bedrock guardrails plus Region-scoped IAM roles supports policy enforcement and auditable, Region-restricted access. Storing documents in Amazon S3 in the applicant's Region and using tagging/metadata provides durable, auditable storage aligned to strict Regional residency requirements and supports the required scale and availability.



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?

  1. 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.
  2. 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.
  3. 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.
  4. 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.

Answer(s): B

Explanation:

Using a single access control JSON file for the S3 data source lets Amazon Q Business enforce document- level access based on IAM Identity Center group membership without managing per-folder files or complex IAM permission sets. Mapping each department prefix to its corresponding Identity Center group in one centralized acl.json minimizes ongoing operational effort while ensuring users only see content from their department.



A healthcare company is using Amazon Bedrock to build a system to help practitioners make clinical decisions. The system must provide treatment recommendations to physicians based only on approved medical documentation and must cite specific sources. The system must not hallucinate or produce factually incorrect information.

Which solution will meet these requirements with the LEAST operational overhead?

  1. Integrate Amazon Bedrock with Amazon Kendra to retrieve approved documents. Implement custom post- processing to compare generated responses against source documents and to include citations.
  2. Deploy an Amazon Bedrock knowledge base and connect it to approved clinical source documents. Use the Amazon Bedrock RetrieveAndGenerate API to return citations from the knowledge base.
  3. Use Amazon Bedrock and Amazon Comprehend Medical to extract medical entities. Implement verification logic against a medical terminology database.
  4. Use an Amazon Bedrock knowledge base with Retrieve API calls and InvokeModel API calls to retrieve approved clinical source documents. Implement verification logic to compare against retrieved sources and to cite sources.

Answer(s): B

Explanation:

Amazon Bedrock Knowledge Bases provides a managed RAG workflow over the approved clinical documentation, and RetrieveAndGenerate automatically grounds generation in the retrieved passages while returning source citations. This minimizes custom retrieval, citation formatting, and verification plumbing compared to building and maintaining separate retrieval and post-processing logic.



A financial services company is developing a real-time generative AI (GenAI) assistant to support human call center agents. The GenAI assistant must transcribe live customer speech, analyze context, and provide incremental suggestions to call center agents while a customer is still speaking. To preserve responsiveness, the GenAI assistant must maintain end-to-end latency under 1 second from speech to initial response display. The architecture must use only managed AWS services and must support bidirectional streaming to ensure that call center agents receive updates in real time.

Which solution will meet these requirements?

  1. Use the Amazon Transcribe streaming API to transcribe calls. Pass the text to Amazon Comprehend to perform sentiment analysis. Feed the results to Anthropic Claude on Amazon Bedrock by using the InvokeModel API. Store results in Amazon DynamoDB. Use a WebSocket API to display the results.
  2. Use Amazon Transcribe streaming with partial results enabled to deliver fragments of transcribed text before customers finish speaking. Forward text fragments to Amazon Bedrock by using the InvokeModelWithResponseStream API. Stream responses to call center agents through an Amazon API Gateway WebSocket API.
  3. Use Amazon Transcribe batch processing to convert calls to text. Pass complete transcripts to Anthropic Claude on Amazon Bedrock by using the ConverseStream API. Return responses through an Amazon Lex chatbot interface that call center agents can access from their work computers.
  4. Use the Amazon Transcribe streaming API with an AWS Lambda function to transcribe each audio segment. Configure the Lambda function to call the Amazon Titan Embeddings model on Amazon Bedrock by using the InvokeModel API. Configure the Lambda function to publish results to an Amazon SNS topic.
    Subscribe the call center agents to the SNS topic.

Answer(s): B

Explanation:

Amazon Transcribe streaming with partial results provides near real-time transcript fragments, and invoking the model with InvokeModelWithResponseStream enables incremental GenAI output as soon as generation starts.
Delivering the streamed output to agents over an API Gateway WebSocket API provides managed, bidirectional streaming updates with minimal latency, supporting the sub-1 second requirement for an initial response.



A media company is launching a platform that allows thousands of users every hour to upload images and text content. The platform uses Amazon Bedrock to process the uploaded content to generate creative compositions.

The company needs a solution to ensure that the platform does not process or produce inappropriate content. The platform must not expose personally identifiable information (PII) in the compositions. The solution must integrate with the company's existing Amazon S3 storage workflow.

Which solution will meet these requirements with the LEAST infrastructure management overhead?

  1. Enable the Enhanced Monitoring tool. Use an Amazon CloudWatch alarm to filter traffic to the platform. Use Amazon Comprehend PII detection to pre-process the data. Create a CloudWatch alarm to monitor for Amazon Comprehend PII detection events. Create an AWS Step Functions workflow that includes an Amazon Rekognition image moderation step.
  2. Use an Amazon API Gateway HTTP API with request validation templates to screen content before storing the uploaded content in Amazon S3. Use Amazon SageMaker AI to build custom content moderation models that process content before sending the processed content to Amazon Bedrock.
  3. Create an Amazon Cognito user pool that uses pre-authentication AWS Lambda functions to run content moderation checks. Use Amazon Textract to filter text content and Amazon Rekognition to filter image content before allowing users to upload content to the platform.
  4. Create an AWS Step Functions workflow that uses built-in Amazon Bedrock guardrails to filter content. Use
    Amazon Comprehend PII detection to pre-process the content. Use Amazon Rekognition image moderation.

Answer(s): D

Explanation:

AWS Step Functions provides a managed orchestration layer that can integrate directly with the existing S3- based upload workflow and run consistent pre- and post-processing steps at scale with minimal infrastructure to manage. Amazon Bedrock guardrails can block inappropriate text outputs, Amazon Comprehend can detect and redact PII from text before inference, and Amazon Rekognition moderation can screen uploaded images so the platform does not process or generate inappropriate content while preventing PII exposure.



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