AWS Certified Generative AI Developer Professional Exam Prep

• Course Overview

• This course rigorously prepares you for the AWS Certified Generative AI Developer Professional exam, offering a complete guide to mastering GenAI development on AWS.
• Master building, deploying, and managing advanced generative AI solutions efficiently across the Amazon Web Services cloud environment.
• Gain deep, practical knowledge of AWS Bedrock, SageMaker, and other integrated AI services, reflecting the latest February 2026 updates.

• Requirements / Prerequisites

• Foundational AWS Knowledge: Solid understanding of S3, EC2, IAM, Lambda, and basic AWS networking is recommended.
• Python Programming Proficiency: Essential for AWS SDKs, custom model development, and implementing AI logic.
• Basic Machine Learning Concepts: Familiarity with model training, inference, and data preprocessing is beneficial.
• Generative AI Fundamentals: Preliminary grasp of LLMs, transformer architectures, and prompt engineering is advantageous.
• Active AWS Account: Required for extensive hands-on labs and deploying resources to gain practical experience.

• Skills Covered / Tools Used

• AWS Bedrock Integration: Programmatically interact with diverse foundation models (FMs) via API for various generative tasks.
• Advanced Prompt Engineering: Master techniques for text, image, and code generation, including few-shot learning and context management.
• Retrieval Augmented Generation (RAG): Implement RAG architectures using Bedrock Knowledge Bases for enhanced, accurate FM responses.
• Bedrock Agents & Orchestration: Design intelligent agents with AWS Bedrock to automate complex multi-step workflows and tool integrations.
• AWS SageMaker for GenAI: Utilize SageMaker for custom model training, fine-tuning, and scalable deployment of generative models.
• SageMaker JumpStart & Endpoints: Accelerate GenAI development with pre-trained models and manage real-time inference endpoints.
• AWS AI Services Integration: Seamlessly connect GenAI solutions with Amazon Comprehend, Polly, Lambda, S3, and Step Functions.
• GenAI MLOps: Apply best practices for model versioning, CI/CD, monitoring, and performance optimization of GenAI workloads.
• Security & Cost Optimization: Implement robust security with KMS/IAM and manage operational costs for GenAI deployments on AWS.

• Benefits / Outcomes

• Achieve Certification Success: Confidently pass the AWS Certified Generative AI Developer Professional exam, validating your expertise.
• Proficient AWS GenAI Developer: Become skilled in building, deploying, and managing a wide array of generative AI applications.
• Master AWS GenAI Ecosystem: Deeply understand and effectively leverage Bedrock, SageMaker, and other AWS AI services.
• Practical AI Solution Development: Translate theoretical knowledge into deployable, innovative AI solutions for real-world problems.
• Enhanced Career Opportunities: Significantly boost your marketability for specialized GenAI Developer and ML Engineer roles.
• Architect Scalable AI: Design secure, cost-effective, and high-performance architectures for GenAI workloads on AWS.
• Stay Ahead in AI: Master the latest generative AI tools and techniques on the leading cloud platform for future advancements.

• PROS

• Comprehensive Exam Preparation: Provides a focused curriculum covering all AWS Certified Generative AI Developer Professional exam objectives, ensuring targeted preparation and certification success.
• Hands-on Practical Experience: Emphasizes practical application through extensive labs, enabling learners to build real generative AI solutions using Bedrock and SageMaker.
• Up-to-Date Content: Explicitly updated February 2026, reflecting the latest advancements, service updates, and best practices in AWS Generative AI services.
• Expert-Led Instruction: Structured learning guided by experienced professionals, breaking down complex Generative AI and AWS concepts into digestible modules.
• Career Advancement: Equips participants with highly sought-after, specialized skills in Generative AI development on AWS, boosting marketability and career growth.

• CONS

• Significant Time Commitment Required: Given the professional-level certification and breadth of topics, learners need substantial time beyond 22.5 hours for practice and mastering complex concepts.

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