MakendranG

Posted on Jan 11

AWS Certified Generative AI Developer – Professional in 2 Weeks (Part 2: Advanced Learning & Exam Prep)

#aws #certification #machinelearning #ai

This is Part 2 of a 3-part series on my AWS Certified Generative AI Developer - Professional certification journey.

Series Navigation:

Part 1: Exam Overview & Foundation Strategy
Part 2: Advanced Learning & Exam Preparation (You are here)
Part 3: Practical Experience & Success Tips

Table of Contents - Part 2

Phase 2: Deep Dive with AWS Skill Builder (Week 2)
Phase 3: Final Exam Preparation with AWS Exam Prep Plan
Additional Practice: Intensive Mock Exams with Udemy
Premium Practice Exams: The Final Edge
Key Learning Areas Aligned with Exam Domains

In Part 1, I established a solid foundation using the Udemy course. Part 2 focuses on the intensive learning phase using official AWS resources and comprehensive practice exams that prepared me for exam success.

Phase 2: Deep Dive with AWS Skill Builder (Week 2)

After completing the Udemy course, I transitioned to the AWS Generative AI Developer Advanced Learning Plan on AWS Skill Builder. This official AWS resource provided the perfect complement to my Udemy foundation.

Learning Plan Overview

Link: AWS Generative AI Developer Advanced Learning Plan (includes Labs)
Total Duration: 35+ hours of comprehensive content
Format: Digital courses + hands-on labs
Level: Advanced
Language: English

Complete Course Breakdown

1. AWS Generative AI Developer - Analyze Requirements and Design Generative AI Solutions

Duration: 1h 55m | Rating: 4.6/5 (184 reviews)
Focus: Requirements analysis and solution design using AWS services and foundation models
Key Skills: Real-world scenarios, AWS best practices, architectural patterns

2. AWS Generative AI Developer - Select and Configure Foundation Models

Duration: 2h 13m | Rating: 4.5/5 (47 reviews)
Focus: Model selection based on performance, capabilities, and business needs
Key Skills: AWS Lambda, API Gateway, AWS AppConfig, resilience strategies, circuit breakers

3. AWS Generative AI Developer - Implement Data Validation and Processing Pipelines

Duration: 2h 30m | Rating: 4.3/5 (26 reviews)
Focus: Robust data validation and processing pipelines for foundation models
Key Skills: Input quality assurance, multimodal formats, model-specific formatting

4. AWS Generative AI Developer - Design and Implement Vector Store Solutions

Duration: 2h 34m | Rating: 4.1/5 (23 reviews)
Focus: Vector database systems for generative AI and semantic search architectures
Key Skills: Maintenance strategies, metadata frameworks, enterprise data integration

5. AWS Generative AI Developer - Design Retrieval Mechanisms for FM Augmentation

Duration: 3h 40m | Rating: 4.0/5 (17 reviews)
Focus: Retrieval-augmented generation (RAG) systems and knowledge asset optimization
Key Skills: Production-ready implementation patterns, AWS services integration

6. AWS Generative AI Developer - Implement Prompt Engineering Strategies and Governance

Duration: 4h 16m | Rating: 4.2/5 (12 reviews)
Focus: Design, implement, and govern effective prompt systems for foundation models
Key Skills: Amazon Bedrock Prompt Flows, context-aware AI systems, automated quality assurance

7. Lab - Develop Retrieval Augmented Generation (RAG) Applications with Amazon Bedrock Knowledge Bases

Duration: 1h 53m | Rating: 4.6/5 (15 reviews)
Focus: Hands-on RAG application development using AnyCompany knowledge base
Key Skills: Retrieve and RetrieveAndGenerate APIs, question-answering systems

8. AWS Generative AI Developer - Agentic AI Solutions and Tool Integrations

Duration: 2h 17m | Rating: 4.6/5 (18 reviews)
Focus: Autonomous decision-making AI agents and tool integrations
Key Skills: AI agent implementation, autonomous task performance, goal achievement

9. AWS Generative AI Developer - Model Deployment Strategies

Duration: 1h 45m | Rating: 4.0/5 (11 reviews)
Focus: Foundation model invocation, container-based deployment, multi-model implementations
Key Skills: Performance optimization, scalability, cost management, security

10. AWS Generative AI Developer - Enterprise Integration Architectures

Duration: 1h 2m | Rating: 4.6/5 (12 reviews)
Focus: Connecting generative AI systems with existing business applications
Key Skills: Integration patterns, enterprise systems connectivity, security maintenance

11. AWS Generative AI Developer - Foundation Model API Integrations

Duration: 1h 28m | Rating: 4.6/5 (9 reviews)
Focus: Foundation model API integrations and Amazon Bedrock implementation
Key Skills: Request patterns, streaming responses, conversational AI applications

12. AWS Generative AI Developer - Implement Application Integration Patterns and Development Tools

Duration: 2h 52m | Rating: 4.1/5 (8 reviews)
Focus: AI-assisted development tools and enterprise system enhancements
Key Skills: Lambda, Step Functions, Amazon Q Business, Bedrock Data Automation

13. Lab - Develop Conversation Pattern with Amazon Bedrock APIs

Duration: 1h | Rating: 4.5/5 (7 reviews)
Focus: Amazon Nova Lite model implementation for intelligent question answering
Key Skills: Zero-shot prompting, context enhancement, streaming responses, RAG simulation

14. AWS Generative AI Developer - Safe User Interactions with Generative AI Applications

Duration: 2h 20m | Rating: 4.8/5 (8 reviews)
Focus: Protection against malicious inputs, inappropriate content, and prompt injection attacks
Key Skills: Amazon Bedrock Guardrails, AWS WAF, content moderation, toxicity detection

15. AWS Generative AI Developer - Implement Data Security and Privacy Controls

Duration: 2h 2m | Rating: 4.2/5 (8 reviews)
Focus: Comprehensive security using AWS's defense-in-depth strategy
Key Skills: VPC endpoints, IAM policies, Lake Formation, CloudWatch, PII detection

16. AWS Generative AI Developer - Implement AI Governance, Compliance, and Transparency

Duration: 1h 32m | Rating: 4.7/5 (8 reviews)
Focus: Governance and compliance frameworks for Generative AI applications
Key Skills: Organizational policies, regulatory requirements, transparency, accountability

17. Lab - Building Secure and Responsible Gen AI with GuardRails for Amazon Bedrock

Duration: 1h | Rating: 4.6/5 (128 reviews)
Focus: Secure generative AI chatbot development with guardrails
Key Skills: RAG implementation, content filtering, access control, logging, security best practices

18. AWS Generative AI Developer - Implementing Cost Optimization and Resource Efficiency Strategies

Duration: 2h 40m | Rating: 3.8/5 (7 reviews)
Focus: Comprehensive cost optimization for generative AI workloads
Key Skills: Cost management frameworks, resource efficiency, performance maintenance

19. AWS Generative AI Developer - Optimize Application Performance

Duration: 1h 46m | Rating: 4.8/5 (7 reviews)
Focus: Performance optimization through systematic approaches
Key Skills: Pre-computation, retrieval systems, model configuration, API profiling

20. AWS Generative AI Developer - Implement Monitoring Systems

Duration: 1h 31m | Rating: 4.8/5 (7 reviews)
Focus: Comprehensive monitoring systems for generative AI applications
Key Skills: Actionable dashboards, performance baselines, anomaly detection, vector database monitoring

21. AWS Generative AI Developer - Implement Evaluation Systems for Generative AI

Duration: 1h 23m | Rating: 4.5/5 (13 reviews)
Focus: Systematic evaluation and optimization of generative AI applications
Key Skills: Assessment frameworks, continuous evaluation, Amazon Bedrock evaluation, hallucination detection

22. AWS Generative AI Developer - Troubleshoot Generative AI Applications

Duration: 1h 41m | Rating: 4.8/5 (10 reviews)
Focus: Structured troubleshooting from fundamental concepts to advanced techniques
Key Skills: Content handling, foundation model integration, prompt optimization, retrieval diagnostics

My Systematic Approach During Phase 2

Week 2 Daily Schedule (7 days):

Days 1-2: Requirements analysis, model selection, and data pipelines (8 hours)
Days 3-4: Vector stores, RAG systems, and prompt engineering (10 hours)
Days 5-6: Agentic AI, security, and governance (8 hours)
Day 7: Performance optimization, monitoring, and troubleshooting (9 hours)

Detailed Study Method:

Completed all 22 courses and labs in the learning plan sequentially
Performed every hands-on lab exercise in my AWS account
Cross-referenced concepts with the Udemy course materials
Focused on AWS-specific implementation details and best practices
Practiced building complete GenAI solutions end-to-end
Documented key architectural patterns and service integrations
Spent extra time on high-weighted exam domains (Foundation Model Integration 31%, Implementation & Integration 26%)

Why This Two-Phase Combination Was Powerful

Udemy provided: Structured learning path, exam-focused content, expert insights from instructors who passed the exam
AWS Skill Builder offered: Official documentation, authoritative best practices, latest service updates, hands-on AWS environment experience
Together they delivered: Comprehensive theoretical foundation + practical AWS implementation skills
The labs reinforced: Theoretical knowledge with real-world application and troubleshooting experience

Supplementary Hands-On Workshops

Beyond the official learning plan, I also explored these valuable AWS workshops for additional practical experience:

Essential AWS Workshops

Building with Amazon Bedrock: Comprehensive hands-on Bedrock development
Amazon Nova Multimodal Understanding: Latest multimodal AI capabilities
Building GenAI Apps with Foundation Models: End-to-end application development

Interactive Learning with AWS SimuLearn

Prompt Engineering with Amazon Bedrock: Interactive prompt engineering practice
Fine-tuning LLM on SageMaker: Advanced model customization techniques

These workshops provided additional hands-on experience that complemented the official learning plan perfectly.

Phase 3: Final Exam Preparation with AWS Exam Prep Plan

To ensure I was fully prepared for the exam format and question style, I completed the AWS Exam Prep Plan: AWS Certified Generative AI Developer - Professional (AIP-C01).

Exam Prep Plan Overview

Link: Exam Prep Plan: AWS Certified Generative AI Developer - Professional (AIP-C01 - English)
Total Duration: 14h 43m of focused exam preparation
Rating: 5.0/5 (7 reviews)
Format: Structured 4-step preparation approach
Language: English

Complete Exam Prep Plan Breakdown

Step 1: Exam Overview and Foundation

1. Exam Prep Overview: AWS Certified Generative AI Developer - Professional (AIP-C01)

Duration: 5m | Rating: 4.6/5 (223 reviews)
Focus: Exam scope, intended audience, and exam topics review
Key Value: Understanding the complete exam structure and expectations

Step 2: Domain-Specific Review and Practice

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

Domain 1 Review: 1h | Rating: 4.4/5 (187 reviews)
Domain 1 Practice: 1h | Rating: 4.3/5 (82 reviews) - Exam-style questions and flashcards
Domain 1 AWS SimuLearn: 1h | Rating: 4.7/5 (47 reviews) - AI-powered real-world scenarios

Domain 2: Implementation and Integration (26%)

Domain 2 Review: 1h | Rating: 4.5/5 (122 reviews)
Domain 2 Practice: 1h | Rating: 4.5/5 (64 reviews) - Exam-style questions and flashcards
Domain 2 AWS SimuLearn: 1h | Rating: 5.0/5 (12 reviews) - AI-powered customer scenarios

Domain 3: AI Safety, Security, and Governance (20%)

Domain 3 Review: 1h | Rating: 4.2/5 (86 reviews)
Domain 3 Practice: 1h | Rating: 4.5/5 (53 reviews) - Exam-style questions and flashcards

Domain 4: Operational Efficiency and Optimization (12%)

Domain 4 Review: 1h | Rating: 4.6/5 (109 reviews)
Domain 4 Practice: 1h | Rating: 4.7/5 (46 reviews) - Exam-style questions and flashcards

Domain 5: Testing, Validation, and Troubleshooting (11%)

Domain 5 Review: 1h | Rating: 4.2/5 (70 reviews)
Domain 5 Practice: 1h | Rating: 4.6/5 (48 reviews) - Exam-style questions and flashcards

Step 3: Comprehensive Practice Assessments

Official Practice Question Set: AWS Certified Generative AI Developer - Professional (AIP-C01)

Duration: 48m | Rating: 4.5/5 (168 reviews)
Format: 20 questions developed by AWS
Key Features:
- Demonstrates actual certification exam question style
- Detailed feedback for each answer choice
- Recommended resources for deeper understanding
- Can be retaken multiple times with questions in different order

Official Pretest: AWS Certified Generative AI Developer - Professional (AIP-C01)

Duration: 3h | Rating: 3.6/5 (26 reviews)
Format: 75 questions with 180-minute time limit
Key Features:
- Same number of questions as actual certification exam
- Same time limit and scaled scoring method as real exam
- Pass/fail scoring to gauge readiness
- Detailed feedback and recommended resources
- Can be retaken multiple times

Step 4: Final Preparation

Exam Prep Summary: AWS Certified Generative AI Developer - Professional (AIP-C01)

Duration: 5m | Rating: 4.2/5 (55 reviews)
Focus: Final preparation checklist and exam day readiness

What Made AWS SimuLearn Unique

AI-Powered Learning: Generative AI helps develop soft skills like communication and problem-solving
Real-World Scenarios: Life-like conversations with AI-generated customers
Hands-On Validation: Build and validate solutions in live AWS Management Console
AI Assistance: AI quiz agent evaluates responses, Dr. Newton provides help when stuck
Professional Tools: Same tools used by technology professionals for AWS solutions

My Systematic Approach During Phase 3

Completed the exam overview to understand the 4-step preparation approach
Worked through each domain review systematically, focusing on high-weight domains first
Practiced with domain-specific questions and flashcards to identify knowledge gaps
Engaged with AWS SimuLearn scenarios for Domains 1 and 2 (highest weighted)
Took the Official Practice Question Set multiple times to familiarize with question style
Completed the Official Pretest as a final readiness assessment
Used detailed feedback to review weak areas and recommended resources

📝 Study Notes: My detailed handwritten notes from all three phases, including domain-specific concepts and exam strategies, are available in my GitHub Study Notes Repository.

Additional Practice: Intensive Mock Exams with Udemy

After completing the AWS Skill Builder exams and gaining a solid understanding of how to approach exam questions and answers, I decided to get additional practice with a comprehensive mock exam course on Udemy.

Course: AWS Certified Generative AI Developer Pro - 4 Mock Exams

Course Link: AWS Certified Generative AI Developer Pro - 4 Mock Exams

Course Statistics & Credibility:

Rating: 4.9/5 (29 ratings) - Exceptionally high rating
Students: 605 students enrolled
Status: Hot & New course
Last Updated: December 2025 (Very recent and current)
Instructor Credentials: Dual AWS AI Early Adopter - Among the First 5,000 Globally
Instructor Achievement: AWS Certified Generative AI Developer - Professional (Early Adopter) - December 2025
Additional Credentials: AWS Certified AI Practitioner (Early Adopter) - November 2024, Google Cloud Generative AI Leader
Teaching Experience: 10+ Cloud and AI certifications, 10 years teaching experience, 180,000+ students

What's Included in This Mock Exam Course

Total Questions: 275 unique, high-quality practice questions
4 Practice Tests: Comprehensive coverage across all exam domains
Assignments: Additional practice exercises
Mobile Access: Study on-the-go capability
Full Lifetime Access: Permanent access to all content
30-Day Money-Back Guarantee: Risk-free investment

Comprehensive Practice Test Breakdown

1. [Start Here - Easy to Medium] AWS GenAI Foundations for Professional Exam (AIP-C01)

Format: 85 questions (Easy to Medium difficulty)
Focus: Essential AWS services, features, and GenAI concepts
Purpose: Foundations warm-up to build confidence

2. [Unofficial] AWS Certified Generative AI Developer - Professional (AIP-C01) - Practice Exam 1

Format: 85 questions (matching official exam length and difficulty)
Focus: Full-spectrum coverage of all exam domains
Purpose: Realistic exam simulation

3. [Unofficial] AWS Certified Generative AI Developer - Professional (AIP-C01) - Practice Exam 2

Format: 85 questions (matching official exam length and difficulty)
Focus: Alternative question set for comprehensive practice
Purpose: Additional full-length exam experience

4. [Unofficial] AWS Certified Generative AI Developer - Professional (AIP-C01) - Focus on Key Concepts

Format: 20 questions (High difficulty)
Focus: Visual architecture diagrams targeting critical decision points
Purpose: High-yield patterns and advanced concepts

What Made This Course Exceptional

Comprehensive Learning Features:

Detailed Explanations for Every Option: Understand why correct answers are right and incorrect answers are wrong
Direct Links to Official Resources: Access relevant AWS documentation directly from explanations
Full Alignment with Official Exam Guide: Questions meticulously mapped to AIP-C01 exam guide syllabus
Visual Architecture Diagrams: Complex scenarios with architectural decision points
Implementation Scenarios: Strong focus on real-world implementation challenges

Topics Covered (Aligned with Official Exam Weightings):

Content Domain 1: Foundation Model Integration, Data Management, and Compliance
Content Domain 2: Implementation and Integration
Content Domain 3: AI Safety, Security, and Governance
Content Domain 4: Operational Efficiency and Optimization for GenAI Applications
Content Domain 5: Testing, Validation, and Troubleshooting

Key AWS Services Extensively Covered:

Amazon Bedrock: Knowledge Bases, Guardrails, Agents, Prompt Management, Data Automation
SageMaker AI: Clarify, Asynchronous Inference
Core Services: Lambda, Step Functions, OpenSearch Service
Development Tools: Amazon Q Developer
20+ Additional GenAI-Relevant AWS Services

Premium Practice Exams: The Final Edge

After completing the comprehensive mock exams, I discovered another exceptional practice resource that provided the final edge for exam success.

Course: [Practice Exams] AWS Certified Generative AI Developer Pro

Course Link: Available on Udemy (search for "Practice Exams AWS Certified Generative AI Developer Pro" by Stéphane Maarek and Abhishek Singh)

Course Statistics & Credibility:

Rating: 4.6/5 (2,598 learners) - Exceptional rating with substantial student base
Status: Hot & New, Premium course
Last Updated: December 2025 (Most current content available)
Instructors: Co-authored by Stéphane Maarek and Abhishek Singh
Instructor Credentials:
- Collective experience of passing 20 AWS Certifications
- Abhishek Singh passed AIP-C01 on day one of beta release
- Stéphane Maarek: 3,000,000+ students taught, 1,000,000+ reviews

What's Included in This Premium Course

Total Questions: 100 unique, high-quality test questions
2 Practice Tests: Strategically designed for progressive difficulty
Assignments: Additional reinforcement exercises
Mobile Access: Study flexibility with Udemy app compatibility
Guarantee: Pass guarantee if you score 90%+ on each practice exam

Practice Test Breakdown

Practice Test #0 - Warm Up - AWS Certified Generative AI Developer Professional

Purpose: Confidence building and concept reinforcement
Focus: Essential concepts with moderate difficulty
Strategy: Identify knowledge gaps before final preparation

Practice Test #1 - Full-Exam - AWS Certified Generative AI Developer Professional

Purpose: Complete exam simulation
Focus: Full-spectrum coverage matching actual exam difficulty
Strategy: Final readiness assessment and timing practice

What Makes These Practice Exams Exceptional

Human-Crafted Excellence:

Expert-Designed Questions: Created by instructors with deep AWS expertise, not AI-generated content
Authentic Exam Feel: Questions mirror actual certification exam tone, complexity, and trap patterns
Blueprint Alignment: Perfectly aligned with official exam guide and domain weightings

Key Learning Areas Aligned with Exam Domains

Based on my comprehensive study across all resources, here are the critical learning areas organized by exam domain:

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

Core Topics:

Amazon Bedrock Service Architecture: Understanding model hosting, API endpoints, and service limits
Foundation Model Selection: Choosing appropriate models based on use case, performance, and cost
Data Ingestion and Processing: Handling various data formats for model consumption
Compliance and Governance: Implementing data privacy, retention policies, and regulatory compliance
Model Lifecycle Management: Versioning, deployment strategies, and rollback procedures

Key AWS Services:

Amazon Bedrock (Runtime, Knowledge Bases, Agents)
Amazon S3 (data storage and versioning)
AWS IAM (access control and permissions)
AWS CloudTrail (audit logging)
Amazon VPC (network isolation)

Domain 2: Implementation and Integration (26%)

Core Topics:

API Integration Patterns: REST APIs, streaming responses, and error handling
Serverless Architectures: Lambda functions, Step Functions, and event-driven patterns
RAG Implementation: Vector databases, embeddings, and retrieval mechanisms
Agentic AI Development: Building autonomous agents with tool integrations
Enterprise Integration: Connecting GenAI with existing business systems

Key AWS Services:

AWS Lambda (serverless compute)
Amazon API Gateway (API management)
AWS Step Functions (workflow orchestration)
Amazon OpenSearch Service (vector search)
Amazon EventBridge (event routing)

Domain 3: AI Safety, Security, and Governance (20%)

Core Topics:

Guardrails Implementation: Content filtering, toxicity detection, and prompt injection protection
Security Best Practices: Encryption, network security, and access controls
Responsible AI: Bias detection, fairness, and transparency
Monitoring and Auditing: Logging, compliance reporting, and governance frameworks
Data Privacy: PII detection, data anonymization, and GDPR compliance

Key AWS Services:

Amazon Bedrock Guardrails
AWS WAF (web application firewall)
Amazon CloudWatch (monitoring and logging)
AWS Config (compliance monitoring)
Amazon Macie (data classification)

Domain 4: Operational Efficiency and Optimization (12%)

Core Topics:

Cost Optimization: Resource sizing, caching strategies, and usage monitoring
Performance Tuning: Latency optimization, throughput improvement, and scaling strategies
Resource Management: Auto-scaling, load balancing, and capacity planning
Caching Strategies: Prompt caching, response caching, and data caching
Monitoring and Alerting: Performance metrics, anomaly detection, and automated responses

Key AWS Services:

Amazon CloudWatch (metrics and alarms)
AWS Auto Scaling (automatic resource adjustment)
Amazon ElastiCache (caching layer)
AWS Cost Explorer (cost analysis)
AWS Trusted Advisor (optimization recommendations)

Domain 5: Testing, Validation, and Troubleshooting (11%)

Core Topics:

Model Evaluation: Quality metrics, performance benchmarks, and A/B testing
Testing Strategies: Unit testing, integration testing, and end-to-end testing
Troubleshooting Techniques: Log analysis, error diagnosis, and performance debugging
Quality Assurance: Automated testing, regression testing, and validation frameworks
Continuous Improvement: Feedback loops, model retraining, and iterative enhancement

Key AWS Services:

Amazon Bedrock Evaluations
AWS X-Ray (distributed tracing)
Amazon CloudWatch Logs (log analysis)
AWS CodePipeline (CI/CD)
Amazon SageMaker (model evaluation)

Continue to Part 3: Practical Experience & Success Tips where I share hands-on lab experiences, practical tips for exam success, and insights about the actual exam experience.

Have questions about the advanced learning phase or practice exams? Feel free to reach out in the comments below!