AWS Certified AI Practitioner Study Plan¶

6-Week Accelerated Study Schedule¶

Week 1: AI/ML Fundamentals and AWS AI Ecosystem¶

Day 1-2: Core AI/ML Concepts¶

• Study AI vs ML vs Deep Learning vs Generative AI
• Learn types of machine learning (supervised, unsupervised, reinforcement)
• Understand ML workflow: data collection, training, evaluation, deployment
• Study common ML algorithms and use cases
• Review Notes: ai-ml-fundamentals.md

Day 3-4: AWS AI Services Overview¶

• Study AWS AI service categories and capabilities
• Learn when to use pre-built AI services vs custom models
• Understand service integration patterns
• Explore AWS AI service use cases and examples
• Hands-on: Explore AWS console AI/ML services
• Review Notes: aws-ai-services.md

Day 5-7: Generative AI Fundamentals¶

• Study generative AI concepts and applications
• Learn about foundation models and their capabilities
• Understand prompt engineering basics
• Study inference parameters (temperature, top-p, top-k)
• Learn about Retrieval Augmented Generation (RAG)
• Review Notes: bedrock.md

Week 2: Amazon Bedrock and Foundation Models¶

Day 1-2: Amazon Bedrock Deep Dive¶

• Study Bedrock architecture and model providers
• Learn about available foundation models (Claude, Jurassic, Command, Llama, Stable Diffusion)
• Understand model selection criteria for different use cases
• Study Bedrock pricing models and cost optimization
• Hands-on: Access Bedrock models in console
• Lab: Basic text generation with different models

Day 3-4: Prompt Engineering and Fine-tuning¶

• Master prompt engineering techniques
• Learn few-shot and zero-shot learning
• Study model customization options
• Understand fine-tuning vs RAG approaches
• Practice: Create effective prompts for various tasks
• Lab: Implement prompt templates and testing

Day 5-7: RAG and Knowledge Bases¶

• Study RAG architecture and benefits
• Learn about vector databases and embeddings
• Understand Bedrock Knowledge Bases
• Study document ingestion and processing
• Hands-on: Build simple RAG application
• Lab: Create knowledge base with sample documents

Week 3: Amazon SageMaker and ML Platform¶

Day 1-2: SageMaker Platform Overview¶

• Study SageMaker components and workflow
• Learn about SageMaker Studio IDE
• Understand built-in algorithms and frameworks
• Study model training and deployment options
• Hands-on: Explore SageMaker Studio interface
• Review Notes: sagemaker.md

Day 3-4: SageMaker Automated ML¶

• Study SageMaker Autopilot capabilities
• Learn about SageMaker Canvas (no-code ML)
• Understand SageMaker JumpStart pre-trained models
• Practice selecting appropriate automation tools
• Lab: Build model with Autopilot
• Lab: Use Canvas for business user scenario

Day 5-7: Model Deployment and Inference¶

• Study SageMaker endpoints and hosting options
• Learn about real-time vs batch inference
• Understand model monitoring and versioning
• Study A/B testing and model updates
• Lab: Deploy model to endpoint
• Practice: Inference patterns and optimization

Week 4: AI Services - Computer Vision, NLP, and Speech¶

Day 1-2: Computer Vision Services¶

• Study Amazon Rekognition capabilities
• Learn image and video analysis features
• Understand custom labels and model training
• Study Amazon Textract for document analysis
• Hands-on: Image analysis with Rekognition
• Lab: Extract data from documents with Textract
• Review Notes: computer-vision.md

Day 3-4: Natural Language Processing¶

• Study Amazon Comprehend text analysis
• Learn sentiment analysis and entity detection
• Understand Amazon Translate capabilities
• Study custom entity recognition
• Hands-on: Text analysis with Comprehend
• Lab: Multi-language translation scenarios
• Review Notes: nlp.md

Day 5-7: Speech and Conversational AI¶

• Study Amazon Transcribe speech-to-text
• Learn Amazon Polly text-to-speech
• Understand Amazon Lex chatbot platform
• Study voice and speech customization options
• Hands-on: Audio transcription with Transcribe
• Lab: Build simple chatbot with Lex
• Review Notes: speech-audio.md

Week 5: Responsible AI, Security, and Governance¶

Day 1-2: Responsible AI Principles¶

• Study AI ethics and fairness concepts
• Learn about bias detection and mitigation
• Understand transparency and explainability
• Study human-in-the-loop approaches
• Review AWS responsible AI practices
• Review Notes: responsible-ai.md

Day 3-4: AI Security and Privacy¶

• Study data privacy in AI systems
• Learn about model security best practices
• Understand encryption for AI data and models
• Study IAM for AI services
• Practice: Configure secure AI service access
• Lab: Implement data protection for AI workloads

Day 5-7: Governance and Compliance¶

• Study AI governance frameworks
• Learn about compliance requirements (GDPR, HIPAA, etc.)
• Understand audit logging for AI services
• Study cost management and optimization
• Practice: Design compliant AI architecture
• Lab: Monitor AI service usage and costs

Week 6: Integration, Practice, and Exam Preparation¶

Day 1-2: AI Solution Integration¶

• Study application integration patterns
• Learn API integration for AI services
• Understand event-driven AI architectures
• Study multi-service AI solutions
• Practice: Design end-to-end AI solutions
• Review Notes: ai-integration.md

Day 3-4: Practice Exams and Scenarios¶

• Take first full practice exam
• Review incorrect answers and concepts
• Study real-world use case scenarios
• Practice service selection for requirements
• Target: Score 70%+ on first practice exam
• Review: scenarios.md

Day 5-6: Final Review and Practice¶

• Take second practice exam
• Deep dive into weak areas identified
• Review key concepts and service capabilities
• Practice exam time management
• Create quick reference notes
• Target: Score 80%+ consistently

Day 7: Exam Day Preparation¶

• Light review of key concepts only
• Review service selection decision trees
• Confirm exam logistics and setup
• Get adequate rest and mental preparation

Daily Study Routine (2-3 hours/day)¶

Weekday Schedule¶

1. 45 minutes: Read documentation and watch videos
2. 60 minutes: Hands-on labs and console exploration
3. 30 minutes: Practice questions and review
4. 15 minutes: Note-taking and concept reinforcement

Weekend Extended Sessions (4-5 hours)¶

1. 90 minutes: Comprehensive hands-on labs
2. 90 minutes: Practice exams and detailed review
3. 60 minutes: Real-world scenario practice
4. 30 minutes: Weak area remediation

Hands-on Lab Schedule¶

Week-by-Week Lab Focus¶

Week 1 Labs: Foundation¶

1. AWS Console navigation for AI services
2. Basic generative AI text generation
3. Simple prompt engineering exercises

Week 2 Labs: Bedrock Deep Dive¶

1. Multi-model comparison with Bedrock
2. Advanced prompt engineering patterns
3. RAG implementation with knowledge base
4. Document Q&A application

Week 3 Labs: SageMaker Platform¶

1. SageMaker Autopilot automated training
2. Canvas no-code model building
3. Model deployment and inference
4. JumpStart pre-trained model usage

Week 4 Labs: AI Services¶

1. Image analysis with Rekognition
2. Document data extraction with Textract
3. Text sentiment analysis with Comprehend
4. Chatbot creation with Lex
5. Audio transcription with Transcribe

Week 5 Labs: Responsible AI¶

1. Security configuration for AI services
2. Cost monitoring and optimization setup
3. Audit logging configuration
4. Bias detection in sample datasets

Week 6 Labs: Integration¶

1. Multi-service AI workflow
2. API Gateway + Lambda + AI service integration
3. End-to-end AI application deployment

Study Resources by Priority¶

Primary Resources¶

1. AWS Skill Builder: Official AI Practitioner exam prep course
2. AWS AI Services Documentation: Service-specific deep dives
3. AWS AI/ML Blog: Latest features and best practices
4. Hands-on Labs: Console exploration and service usage

Secondary Resources¶

1. AWS Whitepapers: AI and ML on AWS
2. AWS Training Videos: re:Invent and Summit sessions
3. Practice Exams: Official AWS practice test
4. Third-party Courses: Supplemental video training

Progress Tracking¶

Weekly Milestones¶

• Week 1: Understand AI/ML fundamentals and generative AI concepts
• Week 2: Master Amazon Bedrock and foundation models
• Week 3: Proficient with SageMaker platform capabilities
• Week 4: Knowledgeable about AI services for vision, NLP, speech
• Week 5: Understand responsible AI and governance
• Week 6: Ready for exam with 80%+ practice scores

Practice Exam Targets¶

• Week 4 end: Score 65%+ on practice exams
• Week 5 end: Score 75%+ on practice exams
• Week 6 end: Score 85%+ consistently

Key Focus Areas for Exam Success¶

Service Selection Mastery¶

• Know when to use Bedrock vs SageMaker
• Understand pre-built AI services vs custom models
• Practice matching services to use case requirements

Generative AI Concepts¶

• Foundation model capabilities and limitations
• Prompt engineering techniques
• RAG architecture and benefits
• Model customization approaches

Responsible AI¶

• Ethics and fairness in AI
• Bias detection and mitigation
• Privacy and security best practices
• Governance frameworks

Cost and Performance¶

• AI service pricing models
• Optimization strategies
• Performance tuning basics

Final Exam Checklist¶

One Week Before¶

• Complete all practice exams with target scores
• Review weak areas from practice tests
• Confirm exam appointment details
• Prepare exam environment (for online proctoring)

Day Before Exam¶

• Light review of service capabilities
• Review cheat sheets and quick references
• Test exam technology setup
• Get adequate sleep

Exam Day¶

• Arrive early or log in 30 minutes before
• Read questions carefully for key requirements
• Use elimination strategy for multiple choice
• Flag uncertain questions for review
• Manage time effectively (~90 seconds per question)

Exam Tips¶

Question Analysis¶

• Identify scenario requirements and constraints
• Look for keywords indicating specific services
• Consider responsible AI and security requirements
• Think about cost and scalability implications

Common Question Patterns¶

• Service selection for specific use cases
• Generative AI and foundation model questions
• Responsible AI practices and ethics
• Security and compliance requirements
• Integration and deployment scenarios