AWS Certified Generative AI Developer – Professional (AIP-C01)
Course Curriculum
20 modules designed to master the subject.
Module 1: Exam Orientation and AWS Basics
Certification structure, exam domains, and foundational AWS knowledge for GenAI developers.
Scaling the Mountain: AWS Certified Generative AI Developer – Professional Overview
Master the blueprint of the AIP-C01 exam. Learn what it takes to become a certified AWS Generative AI Developer at the Professional level.
The Anatomy of Success: Exam Domains and Weightings
Master the blueprint. Understand the high-stakes domains of the AIP-C01 exam and where to focus your study efforts for maximum impact.
Mastering the Clock: Format, Scoring, and Passing Strategy
Learn how to navigate the 3-hour marathon of the AIP-C01 exam. From time management to the art of elimination, this lesson builds your mental edge.
Foundations of the Cloud: Account Setup and AI Sandbox
Get your hands dirty. Learn how to correctly configure your AWS environment, enable model access, and establish the security baseline for GenAI development.
Module 2: Foundation Model Basics
Understanding foundation models, use cases, and selection criteria (hosted vs. managed vs. custom).
The DNA of AI: What are Foundation Models?
Go beyond the buzzwords. Understand the architecture, scale, and multi-modal capabilities that define a Foundation Model in the AWS ecosystem.
The Art of Choice: Model Use Cases and Selection Criteria
Not all models are equal. Learn the professional framework for selecting the right foundation model based on latency, cost, reasoning, and context requirements.
Where Does the Brain Live? Hosted vs. Managed vs. Custom Models
Master the deployment spectrum. Learn when to use the serverless simplicity of Amazon Bedrock versus the full infrastructure control of Amazon SageMaker.
Module 3: Data Management for GenAI
ETL pipelines, data normalization, vector stores, and embeddings for generative AI workloads.
The Fuel for the Fire: Building Data Pipelines and ETL for AI
Data is the differentiator. Learn how to architect professional ETL pipelines using AWS Glue, S3, and Lambda to power your Generative AI applications.
The Polish: Data Cleansing, Normalization, and Indexing
High-quality retrieval starts with high-quality data. Master the art of stripping noise, normalizing text, and preparing indices for the ultimate RAG performance.
The Memory of AI: Vector Stores and Embeddings
Master the math behind the meaning. Deep dive into vector embeddings, similarity search, and choosing between OpenSearch, Aurora, and Pinecone for your AWS GenAI architecture.
Module 4: Knowledge Bases and RAG Architectures
Deep dive into RAG concepts, semantic retrieval, and context assembly in AWS.
The Grounding of AI: Retrieval-Augmented Generation (RAG) Concepts
Stop the hallucinations. Learn how RAG connects your private enterprise data to a Foundation Model to provide accurate, grounded, and citeable answers.
Architecting the Brain: Designing and Indexing Knowledge Bases
Master the structure of your retrieval engine. Learn how to design robust Knowledge Bases in Amazon Bedrock and select the optimal chunking strategy for complex data.
The Precision Search: Context Assembly and Semantic Retrieval
Master the final stage of the RAG pipeline. Learn how to optimize retrieval parameters, implement hybrid search, and assemble the perfect context for your model.
Module 5: Compliance and Data Governance
Handling sensitive data, privacy frameworks, metadata management, and audit requirements.
The Fortress: Handling Sensitive Data and Privacy
Protect your customers and your company. Learn the architectural patterns for identifying, masking, and securing sensitive data in your GenAI pipelines.
The Rulebook: Compliance Frameworks and AWS Tools
Navigate the regulatory landscape of AI. Learn how to use AWS Audit Manager, AWS Artifact, and the Shared Responsibility Model to meet GDPR, HIPAA, and SOC2 requirements.
The Paper Trail: Metadata and Audit Requirements
Master the art of observability and accountability. Learn how to implement model invocation logging, CloudTrail auditing, and robust metadata tagging for your AI workloads.
Module 6: Deploying GenAI Workloads
Choosing between Bedrock and SageMaker, integration patterns, and sync/async request handling.
The Deployment Dilemma: Bedrock vs. SageMaker vs. Custom
Architecture is about trade-offs. Learn the professional decision framework for choosing the right deployment stack for your production AI workloads.
The Bridge: Integrating Foundation Models into Applications
Master the integration layer. Learn how to connect your business logic to Foundation Models using Boto3, handle API errors gracefully, and manage your prompt versions like code.
Speed vs. Substance: Sync, Async, and Streaming Patterns
Master the timing of AI. Learn when to use synchronous, asynchronous, and streaming response patterns to balance user experience, cost, and technical limits.
Module 7: Multi-Step GenAI Workflows
Orchestrating AI with Step Functions and Lambda, API design, and fallback strategies.
The Logic Engine: Orchestration with Step Functions and Lambda
Going beyond the single call. Learn how to architect multi-stage AI workflows using AWS Step Functions to solve complex reasoning tasks with reliability.
The Interface of AI: API Design and Integration Best Practices
Master the standard for AI-powered APIs. Learn how to implement semantic caching, enforce structured outputs, and secure your endpoints against prompt injection.
Resilience in the Dark: FM Routing and Fallback Strategies
Architecture for 100% uptime. Learn how to implement model routing to save costs and fallback strategies to ensure your application survives outages and model failures.
Module 8: Agentic AI Patterns
Designing autonomous agents, activity planning, tool integration, and state management.
The Brain at Work: Agent Design and Activity Planning
From Chatbots to Agents. Learn how to architect autonomous AI systems that can reason, plan, and use tools to solve complex business problems.
The Hands of AI: Tool Integration and Stateful Decision Systems
Give your AI the ability to act. Learn how to define API schemas for tool integration and manage state across complex, multi-turn agent conversations.
The Guardian: Monitoring Agent Execution
Shed light on the black box. Learn how to use Bedrock Traces, CloudWatch, and X-Ray to monitor the complex reasoning and tool-calling behavior of your agents.
Module 9: Identity and Access Control
IAM best practices, fine-grained policies, and resource isolation for AI workloads.
The Zero Trust Foundation: IAM Best Practices for GenAI
Security is non-negotiable. Learn how to implement the Principle of Least Privilege for your AI models, data sources, and Lambda executors using AWS IAM.
Precision Security: Fine-grained Policies and Condition Keys
Master the advanced logic of AWS security. Learn how to use IAM Condition Keys to restrict AI access based on VPC, IP, or specific model attributes.
Dividing the Kingdom: Resource Isolation and Multi-tenancy
Master multi-tenant AI architecture. Learn how to safely isolate data and model access for different users, departments, or customers within a single AWS environment.
Module 10: Responsible AI and Guardrails
Bias mitigation, content safety, moderation, and explainability strategies.
The Ethical Compass: Bias Mitigation Strategies
Build fair and equitable AI. Learn how to identify, measure, and mitigate bias in foundation models using AWS tools and professional red-teaming techniques.
The Shield: Content Safety and Moderation
Protect your brand and your users. Learn how to implement Amazon Bedrock Guardrails to filter harmful content, block denied topics, and prevent prompt injection.
Opening the Black Box: Interpretability and Explainability
Why did the AI say that? Master the techniques for making Foundation Models explainable, transparent, and auditable in high-stakes environments.
Module 11: Governance and Human Oversight
Monitoring for responsible AI, human-in-the-loop workflows, and ethical SOPs.
The Watchtower: Monitoring and Reporting for Responsible AI
Governance at scale. Learn how to build CloudWatch dashboards to track safety violations, monitor model drift, and report on the overall health of your Responsible AI system.
The Human Check: Human-in-the-Loop (HITL) Workflows
AI is the co-pilot; humans are the pilot. Learn how to design workflows that automatically escalate complex or low-confidence AI decisions for human review using Amazon A2I.
The Foundation of Trust: Governance Frameworks and SOPs
Prepare for the long haul. Learn how to establish Standard Operating Procedures for AI incidents, document your models for auditors, and align with global AI safety frameworks.
Module 12: Advanced Prompt Engineering
Mastering CoT, few-shot, multi-modal prompting, and model-specific optimization.
The Art of Steering: Advanced Prompt Engineering
Master the subtle science of prompt engineering. Explore Chain-of-Thought, few-shot learning, and multi-modal techniques to extract maximal performance from models.
The Architecture of Instruction: Managing System and User Prompts
Professionalize your prompt strategy. Learn how to separate persona from query, implement dynamic templates, and use Amazon Bedrock Prompt Management for versioning.
Cross-Model Engineering: Optimizing Prompts for Different Models
Master the nuances of model-specific prompting. Learn how to tailor your instructions for Claude, Llama, and Titan to achieve maximal accuracy and lowest cost.
Module 13: Model Tuning and Fine-tuning
Parameter-efficient fine-tuning (PEFT), continued pre-training, and evaluation.
Precision Surgery: Fine-tuning Foundation Models
Go beyond prompting. Learn the technical mechanics of Parameter-Efficient Fine-Tuning (PEFT) and LoRA to customize model behavior within the AWS ecosystem.
Scaling the Mountain: Continued Pre-training
Expand the model's horizon. Learn how to feed massive datasets of domain-specific unlabeled data to a foundation model to create a specialized expert for your industry.
The Scorecard: Evaluating Fine-tuned Models
Is it actually better? Learn how to use Amazon Bedrock Model Evaluation to objectively measure the accuracy, safety, and performance of your custom AI models.
Module 14: Cost and Performance Optimization
Optimizing token usage, latency reduction, and infrastructure benchmarking.
The Lean AI: Optimizing Token Usage and Costs
AI is expensive. Learn the professional techniques for reducing token overhead, implementing smart model routing, and managing your GenAI budget without sacrificing quality.
The Need for Speed: Improving Latency and Throughput
User experience is measured in milliseconds. Learn how to optimize Time to First Token (TTFT), implement provisioned throughput, and leverage specialized hardware like AWS Inferentia.
Scientific Verification: Performance Testing and Benchmarking
Data-driven decisions. Learn how to design custom benchmarks to compare models and use load testing to ensure your AI infrastructure survives production traffic.
Module 15: Multi-Modal Applications
Building apps with image, video, and audio processing using foundation models.
Seeing and Hearing: Building Multi-Modal GenAI Applications
AI beyond the text box. Learn how to architect applications that can process images, video, and audio using multi-modal foundation models in Amazon Bedrock.
Processing the Senses: Image, Video, and Audio with FM
Master the full spectrum of generative AI. Learn how to generate images, analyze video frames, and transform audio into actionable intelligence using AWS services.
Module 16: Advanced Agent Orchestration
Multi-agent systems, long-running tasks, and persistent context management.
The Symphony of Intelligence: Complex Agent Workflows and Multi-Agent Systems
Two brains are better than one. Learn how to architect multi-agent systems where specialized AI agents collaborate to solve complex, enterprise-scale problems.
Persistence in Action: Handling Long-Running Agent Tasks
Patience is a virtue. Learn how to architect agents that can work for minutes or hours without timing out, using Step Functions and persistent state management.
Persistent Intelligence: Agent Memory and Context Management
An AI that remembers. Learn how to implement short-term and long-term memory for your agents using Bedrock session state and Amazon DynamoDB.
Module 17: Multi-Region and Global Architectures
High availability, data residency, and global routing for GenAI workloads.
The Global Guard: Designing for High Availability Across Regions
Prepare for the worst. Learn how to architect multi-region GenAI systems that survive regional outages and service limits using AWS Global Infrastructure.
Border Control: Data Residency and Cross-Region Replication
Keep your data consistent across the globe. Learn how to implement S3 and Vector Database replication while meeting strict data residency and sovereignty requirements.
The Speed of Light: Global Model Routing and Latency Optimization
Zero distance AI. Learn how to use AWS Global Accelerator and Route 53 to route your users to the lowest-latency model endpoint available globally.
Module 18: Future Trends and Emerging Tech
Reasoning models, visual agents, and self-healing AI systems.
The Logical Leap: Reasoning-Specialized Models
Going beyond word prediction. Discover the new frontier of 'System 2' AI—models designed specifically for complex logic, multi-step planning, and rigorous mathematical thinking.
The All-Seeing Brain: Multi-modal Agents and Visual Reasoning
AI with eyes. Learn how to design agents that can 'see' and 'act'—navigating website UIs, interpreting complex blueprints, and performing visual quality control.
The Resilient Mind: Self-Healing and Self-Correcting AI Systems
AI that fixes itself. Learn how to implement reflection and self-correction loops to build agents that can debug their own code and refine their own answers.
Module 19: Amazon Bedrock Data Foundation
High-volume data ingestion, real-time sync, and data quality management at scale.
The Data Tsunami: High-Volume Data Ingestion for Bedrock
From Gigabytes to Terabytes. Learn how to architect high-performance ingestion pipelines to feed massive enterprise data lakes into Amazon Bedrock Knowledge Bases.
The Living Brain: Real-time Knowledge Synchronization
Eliminate stale data. Learn how to implement event-driven architectures to ensure your Amazon Bedrock Knowledge Base is updated within seconds of a data source change.
Garbage In, Garbage Out: Managing Data Quality at Scale
Data quality is AI quality. Learn how to implement automated validation and cleaning pipelines to ensure your AI models are fueled by high-fidelity, accurate information.
Module 20: Specialized Frameworks and Open Source
Using LangChain, LlamaIndex, and deploying OSS models on SageMaker.
Beyond Boto3: LangChain, LlamaIndex, and AutoGPT
Master the AI ecosystem. Learn how to combine specialized open-source frameworks with Amazon Bedrock to build complex, multi-modal, and autonomous agent workflows.
Full Control: Deploying and Scaling Open-Source Models on SageMaker
Master the power of open source. Learn how to deploy models from Hugging Face onto Amazon SageMaker and scale them to handle millions of requests.
Silicon Power: Specialized Hardware (Inferentia and Trainium)
Master the silicon. Learn how to leverage AWS custom-designed chips to reduce costs by 40% and increase throughput for your Generative AI workloads.
Course Overview
Format
Self-paced reading
Duration
Approx 6-8 hours
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