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AIP-C01 Dumps - AWS Certified Generative AI Developer - Professional Practice Exam Questions

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Amazon Web Services AIP-C01 - AWS Certified Generative AI Developer - Professional Braindumps

Amazon Web Services AIP-C01 - AWS Certified Professional Practice Exam

  • Certification Provider:Amazon Web Services
  • Exam Code:AIP-C01
  • Exam Name:AWS Certified Generative AI Developer - Professional
  • Certification Name:AWS Certified Professional
  • Total Questions:128 Questions and Answers With Detailed Explanations
  • Updated on:Based on the current AIP-C01 exam blueprint. Updated on Jul 12, 2026
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Amazon Web Services AIP-C01 This Week Results

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    AWS Certified Generative AI Developer - Professional

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  • 128
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    Amazon Web Services AIP-C01 Questions and Answers

AIP-C01 Question and Answers

Question # 1

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?

Options:

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.

Question # 2

A media company is building an AI-powered content moderation system by using Amazon Bedrock. The system first classifies text by using a small, low-latency model. Then the system escalates requests that have a confidence score below 0.65 to a larger, more expensive model.

The system must respond in near real time for high-confidence results. The system must process low-confidence requests asynchronously. The system must scale to meet sudden spikes in demand. The company wants to optimize costs for the system by invoking the larger model only when required. The company wants to use decoupled components to achieve high resiliency for the system.

Which solution will meet these requirements?

Options:

A.  

Use Amazon API Gateway to invoke the small model synchronously. If the small model’s confidence score is below 0.65, synchronously call the larger model. Use provisioned concurrency to handle traffic spikes.

B.  

Use an AWS Step Functions workflow that has parallel branches to run both the small model and the large model for every request. Choose the large model result when confidence score values differ.

C.  

Send requests to an Amazon SQS queue. Use AWS Fargate to process messages. Invoke the small model first. If the confidence score is below 0.65, place the request in a second SQS queue to process asynchronously by using the large model.

D.  

Deploy both models on Amazon EC2 instances and enable auto scaling. Use a custom application heuristic to route requests to the appropriate instance based on phrase length and keyword rules.

Question # 3

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?

Options:

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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Amazon Web Services AIP-C01 Exam Content Outline and Weightage

Domain Weightage
Fundamentals of AI and ML 20%
Fundamentals of Generative AI 24%
Applications of Foundation Models 28%
Guidelines for Responsible AI 14%
Security, Compliance, and Governance for AI Solutions 14%

 

The AWS Certified Generative AI Developer – Professional (AIP-C01) exam validates advanced skills in designing, building, and deploying generative AI solutions using AWS technologies. It focuses on practical application across key domains such as foundation model integration, implementation and integration, AI safety and governance, operational optimization, and testing and troubleshooting. Candidates are expected to work with real-world architectures like retrieval-augmented generation (RAG), prompt engineering, and scalable AI workflows. The exam emphasizes secure, efficient, and production-ready AI systems, ensuring professionals can deliver high-impact solutions and demonstrate job-ready expertise in modern AI-driven cloud environments.

 


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AIP-C01 FAQs

It is the AWS Certified Generative Developer Professional certification that validates skills in building AI solutions on AWS.

Developers and cloud professionals looking to enhance their expertise in building cloud-based intelligent applications.

It shows that you can design, implement, and manage cloud-based intelligent applications effectively.

Yes, it can help in securing roles in cloud development, AI-enabled solutions, and technology leadership.

The exam is about three hours long and includes multiple choice, multiple response, and scenario-based questions.

Yes, practical experience with AWS services and building applications strengthens your skills and confidence.

Yes, it is valid for three years, after which renewal is required to keep your credentials current.

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