Course Program Overview
AWS Certified Generative AI Developer – Professional Course
- Duration: 5 Months
- Format: Live Online / Classroom / Blended / Corporate
- Sessions: 5 per week
- Session Length: 01 Hour of each
- Tech Stack: AWS Management Console, Amazon Bedrock APIs, AWS Lambda, Amazon Kendra, Amazon OpenSearch Serverless, AWS Enterprise Guardrails, LangChain / LlamaIndex AWS integrations, AWS Secrets Manager.
- Outcome: Industry-grade portfolio + AWS Professional Certification + Career acceleration
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📌 Key Exam Information
Certification Name
AWS Certified Generative AI Developer – Professional
Level
Advanced / Professional-level
Role Alignment
Generative AI Developer, Core AI Engineer, LLM Solutions Architect, Cognitive Automation Specialist
Duration
140 minutes
Exam Format
Complex multiple choice and multi-select engineering scenario questions
Number of Questions
65–75
Passing Score
Scaled score of 750 / 1000
Languages
English, Japanese
🎯 Who Should Take This Course
- Software engineers and application developers aiming to build, secure, and deploy high-throughput generative AI applications on AWS.
- AI practitioners and solutions architects looking to master complex multi-agent frameworks, advanced RAG tuning, and model optimizations.
- Technical professionals seeking to validate specialized, professional-tier authority in production-level Large Language Model (LLM) orchestration.
🧠 Exam Domains & Weightage
18%
Architecture Selection and Framing
Evaluating foundation model parameters, sizing token windows, mapping context limits, and designing multi-modal systems.
32%
Prompt Engineering and Application Development
Structuring complex prompt templates, orchestrating multi-agent chains, API token routing, and implementing dynamic tool calling.
28%
Customization Techniques and RAG Systems
Configuring Retrieval-Augmented Generation (RAG) layouts, fine-tuning model weights, optimizing embeddings, and semantic vector routing.
22%
Security, Governance, and Operational Monitoring
Enforcing context safety filters, tracking model token spends, deploying guardrails, and auditing API payload privacy.
🏆 Benefits of Certification
- Global Recognition: Standing as an official AWS Certified Professional indicates peak elite technical authority in the tech market.
- Career Pathways: Positions you directly for high-tier compensation brackets in roles like Principal Generative AI Architect.
- Practical Skills: Formally validates your production coding capability across Amazon Bedrock APIs, vector stores, and multi-agent chains.
- Cutting-Edge Authority: Directly proves you can take a raw open-source foundation model and secure it into an enterprise application framework.
🌟 Top 5 Reasons to Choose Adian Solutions for this course
Comprehensive Exam Alignment
Our 3-month roadmap is mapped directly to the official AWS Generative AI Developer Professional exam blueprint. Every complex domain, every advanced API parameter, and every autonomous agent architecture is covered in detail, ensuring you’re exam-ready with no gaps.
Certification Guarantee & Retake Support
We’re committed to your success. If you don’t pass this professional-tier exam on your first attempt, we offer extended access to our learning materials and personalized retake support at no extra cost. Our team will guide you through exam registration, help you apply for AWS vouchers (if available), and ensure you’re fully confident before your next attempt.
Hands-On, Real-World Learning
We go beyond theory with practical programming labs using Amazon Bedrock APIs, Amazon OpenSearch Serverless, and framework tooling. You’ll code live multi-agent application backends, optimize embedding pipelines, and deploy custom fine-tuning routines — skills you can showcase immediately in your portfolio.
Capstone Project & Portfolio Development
Learners complete a comprehensive capstone project that integrates live streaming text token interfaces, semantic vector data retrieval layers, and automated multi-agent API execution. This project becomes part of your professional portfolio, giving you a tangible edge in interviews and job applications.
Career & Placement Support
We provide resume workshops, LinkedIn profile optimization, mock interviews, and direct placement support. Top performers may be referred to hiring partners or offered internships on live corporate projects, accelerating your career journey.
Skills You Will Get
You will gain a strong foundation in artificial intelligence by mastering machine learning concepts, computer vision, natural language processing, and conversational AI. Along the way, you’ll learn to work hands-on with Amazon Bedrock APIs, Amazon OpenSearch Serverless, and AWS Lambda to build deployable solutions. These skills will empower you to design, integrate, and implement real-world AI applications that align directly with industry needs and certification standards.
6+ AWS AI Tools
6+ AWS AI Tools
20+ Guided Labs
20+ Guided Labs
4 Capstone Project
4 Capstone Project
10+ Case Studies
10+ Case Studies
Amazon Bedrock API Integration
Amazon Bedrock API Integration
NLP
NLP
Conversational AI
Conversational AI
Computer Vision
Computer Vision
Course Program
(5 Months)
Classroom / Live Online
140+ Hours
This five month program is designed to take learners from foundational generative AI principles to advanced production grade orchestration using AWS. By extending the roadmap, each domain is explored in greater depth, with additional labs and case studies to ensure mastery. The journey begins with architecture framing, progresses into prompt engineering, then RAG systems and customization, followed by governance and monitoring, and finally culminates in a comprehensive Capstone Project with exam preparation.
● Month 1
Exam Coverage: 18–20%
Architecture Selection & Multi Modal Framing
Learners begin with the fundamentals of generative AI architecture. We cover foundation model parameters, token window sizing, context limits, and multi modal system design. Students learn how to evaluate trade offs between model families, embedding strategies, and application scalability.
Practical Labs:
Configure Bedrock API endpoints, size token windows for multi modal tasks, and design architectural blueprints for enterprise generative AI systems.
Case Studies:
Financial firms framing generative AI for compliance reporting; healthcare providers designing multi modal systems for imaging and text synthesis.
● Month 2
Exam Coverage: 20–25%
Prompt Engineering & Application Development
This month focuses on advanced prompt engineering. Learners master complex prompt templates, multi agent orchestration, API token routing, and dynamic tool calling. Emphasis is placed on building production ready applications using Bedrock APIs and AWS Lambda.
Practical Labs:
Build multi agent chains with LangChain, implement dynamic tool calling, and configure token routing strategies.
Case Studies:
Legal discovery pipelines using structured prompts; investment advisory agents orchestrating multi step reasoning workflows.
● Month 3
Exam Coverage: 25–30%
Customization & Retrieval Augmented Generation (RAG)
Learners dive into RAG systems, fine tuning model weights, optimizing embeddings, and semantic vector routing. Students learn to integrate Amazon Kendra and OpenSearch Serverless for enterprise grade retrieval.
Practical Labs:
Configure vector embeddings, build semantic search pipelines, and fine tune models for domain specific tasks.
Case Studies:
Medical documentation synthesizers using RAG; e commerce recommendation engines optimized with embeddings.
● Month 4
Exam Coverage: 20–25%
Security, Governance & Operational Monitoring
This month emphasizes enterprise safety and governance. Learners enforce context safety filters, track token spends, deploy guardrails, and audit API payload privacy. Students practice building secure generative AI applications that meet compliance standards.
Practical Labs:
Implement AWS Enterprise Guardrails, configure Secrets Manager for secure API handling, and deploy monitoring dashboards for token usage.
Case Studies:
Enterprises securing generative AI chat systems against prompt injection; regulated industries enforcing strict audit compliance.
● Month 5
Exam Coverage: Consolidation of 100%
Capstone Project & Exam Preparation
The final month consolidates all skills into a professional portfolio. Learners complete full length mock exams, case study workshops, and targeted labs. The Capstone Project requires designing and deploying a complete generative AI solution integrating architecture framing, prompt engineering, RAG, and governance.
Practical Labs:
Execute full exam simulations, refine orchestration pipelines, and deploy multi agent generative AI systems with monitoring and safety guardrails.
Case Studies:
Autonomous co pilot applications for code generation; global recommendation ecosystems integrating multi modal generative AI.
Capstone Project:
Architect and deploy a production grade generative AI application featuring multi agent orchestration, semantic vector retrieval, and enterprise guardrails.
Real Roles. Real Results.
Explore Your Post-Course Career
After completing the course, learners can unlock high-impact roles such as Machine Learning Engineer, Cloud Solutions Architect, Data Scientist, and AI Product Lead—across sectors like technology, BFSI, healthcare, retail, and manufacturing.
Machine Learning Engineer
Designs, trains, and optimizes ML models for prediction, classification, and automation across industries like finance, healthcare, and manufacturing.
Data Scientist
Extracts insights from structured and unstructured data using statistical analysis, visualization, and modeling techniques. Drives decision-making and business intelligence.
AI Solutions Architect
Leads the design and deployment of scalable AI systems using cloud platforms, MLOps pipelines, and enterprise-grade frameworks.
Deep Learning Researcher
Specializes in neural networks, CNNs, RNNs, GANs, and transformers. Builds models for image recognition, NLP, and generative tasks.
MLOps Engineer
Implements CI/CD pipelines, containerization, and cloud-native deployment for AI models. Ensures reliability, scalability, and performance monitoring.
Generative AI Developer
Builds intelligent applications using LangChain, Hugging Face, and LLMs. Applies RAG pipelines and transformer models to domains like legal tech and marketing.
Computer Vision Engineer
Develops image and video analysis systems using OpenCV, TensorFlow, and deep learning. Works on facial recognition, object detection, and OCR.
NLP Engineer
Creates language models and conversational AI systems using NLTK, SpaCy, and transformers. Powers chatbots, sentiment analysis, and document summarization.
AI Product Lead
Bridges technical teams and business strategy. Oversees AI product lifecycle—from ideation to deployment—ensuring alignment with market needs and ethical standards.
Frequently Asked Questions
AWS Certified Generative AI Developer – Professional
Yes, this is an advanced professional-level software development certification course. Enrollees must possess an elementary understanding of programming concepts—preferably using Python or JavaScript—along with basic cloud terminology. The training dives straight into writing application controllers, managing API response models, and configuring vector data queries.
This is a comprehensive 3-month advanced engineering track delivering 80+ structural training hours. The structure provides 5 interactive live programming lectures per week (1 hour each), deeply backed by hands-on sandbox labs, script optimization reviews, and 4 high-value professional portfolio projects to ensure project readiness.
The official professional-tier exam contains 65 to 75 complex multiple-choice and multiple-select questions within a 140-minute test limit. The questions present highly complicated enterprise business challenges, placing you as a core developer who must choose the absolute best, most secure, or most token-efficient coding path to orchestrate multi-agent solutions.
Completing this certified track opens elite hiring paths, qualifying you for specialized engineering roles such as Generative AI Application Developer, Advanced Prompt Engineer, LLM Solutions Developer, Cognitive Product Engineer, or Core AI Systems Integrator across prominent tech firms, financial hubs, and global consulting groups.
AWS professional-level credentials represent the absolute peak indicator of technical competence in the enterprise cloud market. Passing this exam demonstrates to software engineering managers and recruiters that you possess the hands-on coding ability to build autonomous multi-agent apps, secure data vectors, and manage corporate context guardrails.
In accordance with official AWS certification policies, your Generative AI Developer Professional credential remains fully valid for a period of three years from your verified test-passing date. To keep your professional designation active, you must pass the current exam version before your three-year eligibility window closes.
You will develop direct, practical operational familiarity with development components inside the AWS ecosystem. Throughout the course, you will write custom API orchestration calls inside Amazon Bedrock, manage data vector collections via Amazon OpenSearch Serverless, deploy backend application microservices with AWS Lambda, handle application secrets via AWS Secrets Manager, and build guardrails.
Adian Solutions delivers a complete career preparation ecosystem. This delivers multiple full-length scenario-based mock exams matching real professional testing formats, custom code architecture critiques, technical portfolio host packaging, engineering resume workshops, mock technical coding challenges, LinkedIn profile optimization, and direct job placement support through our recruiter network.
Yes, candidates can schedule and take their official examination via a remote proctored testing framework from home or an isolated corporate office space, provided their local setup matches strict technical rules (including an active webcam stream, audio checking, and an empty workspace layout). Alternatively, you can take it at any physical Pearson VUE testing location.
Your growth as an application engineer is our singular goal. If your custom application code fails to clear a project milestone, Adian Solutions grants extended laboratory platform access and direct live debugging sessions with expert mentors at completely zero extra cost. Our technical mentors will review your scripts, isolate syntax or parameter errors, and guide you until your app passes deployment standards perfectly.
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Partnering with Adian Soft Solutions transformed our AI adoption journey. Their training programs gave our team the confidence to deploy advanced ML models in production. The hands-on labs and real-world case studies were game changers.
Priya Sharma, CTO Global Tech Innovators