Amazon AIP-C01 Exam Dumps

Question # 1

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. 

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Question # 2

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 the 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. 

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Question # 3

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. 

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Question # 4

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 high-relevance 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. 

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Question # 5

A company is using Amazon Bedrock and Anthropic Claude 3 Haiku to develop an AI assistant. The AI assistant normally processes 10,000 requests each hour but experiences surges of up to 30,000 requests each hour during peak usage periods. The AI assistant must respond within 2 seconds while operating across multiple AWS Regions. The company observes that during peak usage periods, the AI assistant experiences throughput bottlenecks that cause increased latency and occasional request timeouts. The company must resolve the performance issues. Which solution will meet this requirement?

A. Purchase provisioned throughput and sufficient model units (MUs) in a single Region. Configure the application to retry failed requests with exponential backoff. 
B. Implement token batching to reduce API overhead. Use cross-Region inference profiles to automatically distribute traffic across available Regions.
C. Set up auto scaling AWS Lambda functions in each Region. Implement client-side round-robin request distribution. Purchase one model unit (MU) of provisioned throughput as a backup. 
D. Implement batch inference for all requests by using Amazon S3 buckets across multiple Regions. Use Amazon SQS to set up an asynchronous retrieval process. 

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Question # 6

A legal research company has a Retrieval Augmented Generation (RAG) application that uses Amazon Bedrock and Amazon OpenSearch Service. The application stores 768- dimensional vector embeddings for 15 million legal documents, including statutes, court rulings, and case summaries. The company's current chunking strategy segments text into fixed-length blocks of 500 tokens. The current chunking strategy often splits contextually linked information such as legal arguments, court opinions, or statute references across separate chunks. Researchers report that generated outputs frequently omit key context or cite outdated legal information. Recent application logs show a 40% increase in response times. The p95 latency metric exceeds 2 seconds. The company expects storage needs for the application to grow from 90 GB to 360 GB within a year. The company needs a solution to improve retrieval relevance and system performance at scale. Which solution will meet these requirements? 

A. Increase the embedding vector dimensionality from 768 to 4,096 without changing the existing chunking or pre-processing strategy. 
B. Replace dynamic retrieval with static, pre-written summaries that are stored in Amazon S3. Use Amazon CloudFront to serve the summaries to reduce compute demand and improve predictability. 
C. Update the chunking strategy to use semantic boundaries such as complete legal arguments, clauses, or sections rather than fixed token limits. Regenerate vector embeddings to align with the new chunk structure. 
D. Migrate from OpenSearch Service to Amazon DynamoDB. Implement keyword-based indexes to enable faster lookups for legal concepts. 

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Question # 7

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.

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Question # 8

A company is using AWS Lambda and REST APIs to build a reasoning agent to automate support workflows. The system must preserve memory across interactions, share relevant agent state, and support event-driven invocation and synchronous invocation. The system must also enforce access control and session-based permissions. Which combination of steps provides the MOST scalable solution? (Select TWO.) 

A. Use Amazon Bedrock AgentCore to manage memory and session-aware reasoning. Deploy the agent with built-in identity support, event handling, and observability. 
B. Register the Lambda functions and REST APIs as actions by using Amazon API Gateway and Amazon EventBridge. Enable Amazon Bedrock AgentCore to invoke the Lambda functions and REST APIs without custom orchestration code. 
C. Use Amazon Bedrock Agents for reasoning and conversation management. Use AWS Step Functions and Amazon SQS for orchestration. Store agent state in Amazon DynamoDB. 
D. Deploy the reasoning logic as a container on Amazon ECS behind API Gateway. Use Amazon Aurora to store memory and identity data. 
E. Build a custom RAG pipeline by using Amazon Kendra and Amazon Bedrock. Use AWS Lambda to orchestrate tool invocations. Store agent state in Amazon S3. 

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Question # 9

An insurance company uses existing Amazon SageMaker AI infrastructure to support a web-based application that allows customers to predict what their insurance premiums will be. The company stores customer data that is used to train the SageMaker AI model in an Amazon S3 bucket. The dataset is growing rapidly. The company wants a solution to continuously re-train the model. The solution must automatically re-train and re-deploy the model to the application when an employee uploads a new customer data file to the S3 bucket. Which solution will meet these requirements?

A. Use AWS Glue to run an ETL job on each uploaded file. Configure the ETL job to use the AWS SDK to invoke the SageMaker AI model endpoint. Use real-time inference with the endpoint to re-deploy the model after it is re-trained on the updated customer dataset. 
B. Create an AWS Lambda function and webhook handlers to generate an event when an employee uploads a new file. Configure SageMaker Pipelines to re-deploy the model after it is re-trained on the updated customer dataset. Use Amazon EventBridge to create an event bus. Set the Lambda function event as the source and SageMaker Pipelines as the target. 
C. Create an AWS Step Functions Express workflow with AWS SDK integrations to retrieve the customer data from the S3 bucket when an employee uploads a new file to the S3 bucket. Use a SageMaker Data Wrangler flow to export the data from the S3 bucket to SageMaker Autopilot. Use the SageMaker Autopilot to re-deploy the model after it has been re-trained on the updated customer dataset. 
D. Create an AWS Step Functions Standard workflow. Configure the first state to call an AWS Lambda function to respond when an employee uploads a new file to the S3 bucket. Use a pipeline in SageMaker Pipelines to re-deploy the model after it has been re-trained on the updated customer dataset. Use the next state in the workflow to run the pipeline when the first state receives a response. 

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Question # 10

A company is using Amazon Bedrock to build a customer-facing AI assistant that handles 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 with content filters set to high to protect against prompt injection attacks. 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 with content filters set to high. Use AWS WAF to block suspicious inputs. Use AWS CloudTrail to log API calls. 
C. Deploy Amazon Comprehend custom classifiers to detect prompt injection attacks. Use Amazon API Gateway request validation. Use CloudWatch Logs to capture intervention events. 
D. Configure Amazon Bedrock guardrails with custom content filters and word filters set to high. Configure cross-Region guardrail replication for failover. Store logs in AWS CloudTrail for compliance auditing. 

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Question # 11

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 RetrieveAndGenerate API 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. 

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Question # 12

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? (Select THREE.)

A. Deploy Amazon Textract and Amazon Augmented AI within the same Region to extract relevant data from the scanned documents. Route low-confidence pages to human reviewers. 
B. 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. 
C. Use Amazon Kendra and Amazon OpenSearch Service to extract field-level values semantically from the uploaded documents before inference. 
D. 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. 
E. 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. 
F. Use Amazon SageMaker Clarify to generate fairness and bias reports based on model scoring decisions that Amazon Bedrock makes. 

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Question # 13

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? 

A. 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. 
B. 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. 
C. 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. 
D. 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. 

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Question # 14

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. Analysts must examine relationships holistically to provide accurate investment guidance. The application must deliver comprehensive answers that capture indirect relationships between financial entities and must respond in less than 3 seconds. Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon Bedrock Knowledge Bases with GraphRAG and Amazon Neptune Analytics to store financial data. Analyze 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 to query multiple vector embeddings in sequence. 
C. Use Amazon OpenSearch Serverless vector search with k-nearest neighbor (k-NN). Implement manual relationship mapping in an application layer that runs on Amazon EC2 Auto Scaling. 
D. Use Amazon DynamoDB to store financial data in a custom indexing system. Use AWS Lambda to query relevant records. Use Amazon SageMaker to generate responses. 

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Question # 15

A financial services company is creating a Retrieval Augmented Generation (RAG) application that uses Amazon Bedrock to generate summaries of market activities. The application relies on a vector database that stores a small proprietary dataset with a low index count. The application must perform similarity searches. The Amazon Bedrock model’s responses must maximize accuracy and maintain high performance. The company needs to configure the vector database and integrate it with the application. Which solution will meet these requirements?

A. Launch an Amazon MemoryDB cluster and configure the index by using the Flat algorithm. Configure a horizontal scaling policy based on performance metrics. 
B. Launch an Amazon MemoryDB cluster and configure the index by using the Hierarchical Navigable Small World (HNSW) algorithm. Configure a vertical scaling policy based on performance metrics. 
C. Launch an Amazon Aurora PostgreSQL cluster and configure the index by using the Inverted File with Flat Compression (IVFFlat) algorithm. Configure the instance class to scale to a larger size when the load increases. 
D. Launch an Amazon DocumentDB cluster that has an IVFFlat index and a high probe value. Configure connections to the cluster as a replica set. Distribute reads to replica instances. 

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