AWS Certified Generative AI Developer - Professional AIP-C01 Exam Overview

The AWS Certified Generative AI Developer - Professional AIP-C01 exam validates the ability to integrate foundation models into applications and business workflows and implement production-ready generative AI solutions using AWS technologies. AWS defines the target role as a GenAI developer who can design, build, secure, integrate, monitor, optimize, and troubleshoot generative AI applications.

Candidates are expected to understand foundation model selection and integration, Retrieval Augmented Generation (RAG), vector stores and embeddings, prompt engineering, agentic AI systems, enterprise integration, AI safety and governance, cost and performance optimization, model evaluation, monitoring, and troubleshooting. Current AWS objectives also include modern integration patterns such as Model Context Protocol (MCP) and production deployment strategies.

Passcert provides updated AIP-C01 Practice Tests and study materials to help candidates review these current professional-level GenAI development skills, identify weaker areas, and supplement official AWS learning resources, documentation, and hands-on experience.

AIP-C01 Exam Information

AWS AIP-C01 Exam Domains

Domain 1: Foundation Model Integration, Data Management, and Compliance — 31%

This is the largest domain of the AIP-C01 exam. It focuses on designing GenAI solutions, selecting and configuring foundation models, preparing data for model consumption, building vector-store architectures, implementing RAG and retrieval workflows, and developing prompt-engineering and prompt-governance strategies. Candidates should understand how services such as Amazon Bedrock, Amazon SageMaker AI, Amazon OpenSearch Service, Amazon Aurora with pgvector, AWS Lambda, and Amazon Bedrock Knowledge Bases can be combined to build flexible and scalable GenAI applications.

Domain 2: Implementation and Integration — 26%

This domain focuses on turning GenAI designs into working production applications. Candidates should understand agentic AI systems, tool integration, MCP, model deployment, API-based FM access, event-driven architectures, enterprise integrations, human-in-the-loop workflows, and application development patterns. AWS specifically includes agent orchestration, model deployment strategies, FM API integration, and enterprise connectivity as part of this domain.

Domain 3: AI Safety, Security, and Governance — 20%

Candidates should know how to implement safe and secure GenAI applications through input/output controls, data security, privacy, governance, compliance, and Responsible AI practices. This includes protecting sensitive information, controlling model behavior, implementing appropriate access controls, and applying safety mechanisms such as Amazon Bedrock Guardrails and AWS security services.

Domain 4: Operational Efficiency and Optimization for GenAI Applications — 12%

This domain focuses on operating GenAI applications efficiently in production. Candidates should understand token optimization, context-window management, model cost/performance trade-offs, latency optimization, resource efficiency, observability, and monitoring. AWS specifically expects candidates to make decisions that balance model capability, response quality, cost, and performance.

Domain 5: Testing, Validation, and Troubleshooting — 11%

Candidates should understand how to evaluate GenAI systems for relevance, factual accuracy, consistency, fluency, safety, latency, cost, and business effectiveness. The domain also covers A/B and canary testing, model evaluation, user-feedback mechanisms, and troubleshooting issues across GenAI application components.

How to Prepare for the AWS AIP-C01 Exam

Master Foundation Models, RAG, and Vector Search

Start with foundation model selection, embeddings, vector databases, chunking, retrieval, reranking, hybrid search, Bedrock Knowledge Bases, and prompt engineering. Domain 1 represents 31% of scored content, making these areas especially important.

Build Agentic AI and Integration Skills

Study agents, tool use, MCP, API integrations, event-driven workflows, enterprise systems, Lambda, Step Functions, API Gateway, and model deployment strategies. Understand how GenAI capabilities are incorporated into existing production applications.

Strengthen Security, Guardrails, and Governance

Review IAM, data privacy, access controls, content safety, Bedrock Guardrails, responsible AI, governance, logging, and compliance. Security decisions should be considered throughout the entire GenAI application lifecycle.

Learn Cost, Performance, and Observability

Practise balancing model quality against token usage, latency, throughput, and cost. Review context optimization, model routing, monitoring, CloudWatch, and operational metrics for production GenAI systems.

Practise Evaluation and Troubleshooting

Understand how to evaluate model outputs using quality and business metrics, compare models and prompts, use A/B or canary testing, gather user feedback, and diagnose failures in retrieval, models, agents, integration, or infrastructure.

Who Should Take the AIP-C01 Exam?

The AIP-C01 exam is designed for GenAI developers, cloud developers, AI engineers, solution developers, and technical professionals responsible for implementing production generative AI applications on AWS.

AWS recommends that candidates have:

● 2 or more years of experience building production-grade applications using AWS or open-source technologies

● General AI/ML or data-engineering experience

● At least 1 year of hands-on experience implementing GenAI solutions

● Experience with AWS compute, storage, networking, security, IaC, observability, and cost optimization

AWS also notes that advanced model training, advanced ML techniques, and extensive feature engineering are outside the target role, reinforcing that AIP-C01 is primarily about integrating and operationalizing foundation models, not becoming a model-training specialist.

How Passcert Supports Your AIP-C01 Preparation

Coverage of Current AWS GenAI Domains

The materials are organized around the current AIP-C01 scope, including foundation models, RAG, vector stores, agents, integration, security, optimization, evaluation, and troubleshooting.

Production GenAI Scenario Practice

Practice questions help candidates review realistic architecture and implementation decisions involving Amazon Bedrock, AWS services, enterprise applications, security controls, and production operations.

PDF and Test Engine Study Options

Use PDF materials for structured review and the Test Engine for interactive practice and progress evaluation.

Regular Content Reviews and Updates

Materials are reviewed when AWS changes GenAI services, exam objectives, certification requirements, or important technologies such as Bedrock, agent frameworks, RAG, or MCP.

Member Area Access and Customer Support

Eligible customers can access available files through the Passcert Member Area and contact support for downloads, Test Engine use, product updates, and related service questions.

Frequently Asked Questions About AWS AIP-C01 Exam

What is the AWS AIP-C01 exam?

AIP-C01 is the exam for AWS Certified Generative AI Developer - Professional. It validates the ability to integrate foundation models into applications and production workflows using AWS technologies.

Who should take the AWS AIP-C01 certification?

The certification is designed for developers, cloud professionals, AI engineers, and technical specialists who build or support generative AI applications on AWS.

What topics are covered in the AWS AIP-C01 exam?

The exam covers foundation models, Amazon Bedrock, generative AI application architecture, RAG, prompt engineering, AI agents, security, monitoring, and responsible AI.

Does AIP-C01 require programming experience?

Yes. AIP-C01 targets professional-level candidates and expects practical knowledge of developing and integrating generative AI applications.

How many questions are on the AIP-C01 exam?

The exam contains 75 total questions, including 65 scored questions and 10 unscored questions.

What experience does AWS recommend?

AWS recommends at least two years of production application development experience plus one year of hands-on GenAI implementation experience.