AWS Machine Learning Associate (MLA) Certification Notes

Comprehensive, exam-focused study notes for the AWS Certified Machine Learning - Associate (MLA-C01) certification exam.

📖 Overview

This repository contains concise, exam-focused study notes for the AWS Machine Learning Associate certification. All content is optimized for efficient studying with:

• ✅ Brief, scannable notes - No lengthy explanations
• ✅ Exam-focused content - Prioritizes exam-relevant information
• ✅ Visual organization - Tables, comparisons, and quick references
• ✅ Comprehensive coverage - All exam domains covered
• ✅ Hands-on guidance - Lab recommendations and practice scenarios

🎯 Quick Start

New to this repository? Start here:

1. 📝 Read the Study Guide for exam overview and 10-week study plan
2. ⚡ Bookmark the Cheat Sheet for quick reference
3. 📚 Work through topics in the Structure section below
4. ✅ Track your progress using the Study Progress checklist

Structure

Core ML Concepts

• Machine Learning Fundamentals
• Model Training & Evaluation
• Feature Engineering

AWS ML/AI Services

• Amazon SageMaker - Custom ML model building
  • Hyperparameters - Algorithm hyperparameters in detail
  • Training & Fine-Tuning - Transfer learning, fine-tuning
  • JumpStart - Pre-built models and solutions
• SageMaker Clarify - Bias & Explainability
• AWS ML Algorithms - All 17 SageMaker built-in algorithms
• AWS AI Services - Comprehend, Rekognition, Lex, Textract, Kendra, Personalize
• AWS Generative AI - Bedrock, Amazon Q (foundation models & LLMs)
• Data Services - S3, Glue, Athena, EMR, Kinesis, Lake Formation, Ground Truth
• MLOps & Deployment - Deployment strategies, inference optimization
  • Experiments & Tracking - SageMaker Experiments, TensorBoard
  • CI/CD - Model Registry, Pipelines, Kubernetes
  • Monitoring - Model Monitor, observability, cost optimization
• Security - IAM, core principles, security services, best practices
  • Encryption - KMS, Secrets Manager, encryption at rest & in transit
  • Network Security - VPC, security groups, endpoints, SageMaker VPC config

Quick References

• 📝 Study Guide - START HERE! Exam strategy & roadmap
• ⚡ Cheat Sheet - Quick reference tables
• 📋 Template - Template for creating new notes

Study Progress

Core ML Knowledge

• Machine Learning Fundamentals
• Model Training & Evaluation
• Feature Engineering

AWS ML Services

• Amazon SageMaker (Custom ML)
  • Hyperparameters (Algorithm configuration)
  • Training & Fine-Tuning (Transfer learning)
  • JumpStart (Pre-built models)
• SageMaker Clarify (Bias Detection)
• AWS ML Algorithms (17 built-in algorithms)

AWS AI Services (Pre-trained)

• NLP Services (Comprehend, Translate, Transcribe, Polly)
• Vision Services (Rekognition, Textract)
• Conversational AI (Lex)
• Search & Recommendations (Kendra, Personalize)
• Specialized (A2I, Lookout, Fraud Detector)
• Generative AI (Bedrock, Amazon Q)

Data & MLOps

• Data Services (S3, Glue, Athena, EMR, Kinesis, Lake Formation, Ground Truth)
• MLOps & Deployment (Deployment strategies, inference optimization)
  • Experiments & Tracking (SageMaker Experiments, TensorBoard)
  • CI/CD (Model Registry, Pipelines, Kubernetes)
  • Monitoring (Model Monitor, observability, cost)
• Security (IAM, Core Principles, Security Services)
  • Encryption (KMS, Secrets Manager, at rest & in transit)
  • Network Security (VPC, Security Groups, VPC Endpoints)

📝 About Code Examples

Code blocks in these notes are for:

• Conceptual understanding - Illustrate how services work
• Parameter reference - Show configuration options you'll see in exam scenarios
• NOT for memorization - You won't write code on the exam

Focus on: Service names, parameter names, workflow concepts - not syntax.

Tags

• #core - Core exam topic
• #exam-tip - Exam-specific insight
• #hands-on - Practice/lab required
• #gotcha - Common pitfall
• #important - High priority

📊 Repository Stats

• Total Notes: 21 comprehensive markdown files
• Total Lines: 9,170 lines of exam-focused content
• Coverage: All 4 AWS MLA exam domains (100%)
• Algorithms Covered: 17 SageMaker built-in algorithms
  • Supervised: Linear Learner, XGBoost, KNN, Factorization Machines
  • Computer Vision: Image Classification, Object Detection, Semantic Segmentation
  • NLP: BlazingText, Seq2Seq, Object2Vec
  • Time Series: DeepAR
  • Unsupervised: K-Means, PCA, LDA, NTM
  • Anomaly Detection: Random Cut Forest, IP Insights
• Services Covered: 25+ AWS ML/AI services
  • Traditional AI Services (Comprehend, Rekognition, Lex, Textract, Kendra, Personalize, etc.)
  • Generative AI (Bedrock: Claude, Titan, Stable Diffusion; Amazon Q family; Agents with aliases)
  • SageMaker ecosystem (Training, Clarify, Ground Truth, Pipelines, Experiments, TensorBoard, JumpStart, Role Manager, Lineage Tracking)
  • Data services (S3, Glue, Athena, EMR, Kinesis, Redshift, Lake Formation)
  • Data Lakes (Lake Formation: column/row security, LF-Tags, permissions)
  • Instance Types (M5, C5, P3, P4d, G4dn, G5, Inf1, Trn1) - Training & inference selection
• Exam Tips: 333 #exam-tip tags throughout
• Study Time: 10-week suggested plan in study guide

🤝 Contributing

Contributions are welcome! To maintain consistency:

1. Follow the format in TEMPLATE.md
2. Keep notes brief and exam-focused
3. Use appropriate tags (#core, #exam-tip, #hands-on, #gotcha, #important)
4. Update cross-references when adding new content

📝 Usage

For Students:

• Browse topics by category in the Structure section
• Use search (Ctrl/Cmd + F) to find specific keywords
• Check off items in Study Progress as you learn
• Review Cheat Sheet before exam day

For AI-Assisted Study:

• Provide keywords or topics, and AI will organize/update notes accordingly
• Example: "Add notes about AWS Forecast" or "Explain concept drift"
• AI follows guidelines in CLAUDE.md for consistency

📂 Project Structure

aws-mla-certification-notes/
├── CLAUDE.md                          # Repository guidance for AI
│
├── core-ml/                           # Core ML Concepts
│   ├── ml-fundamentals.md
│   ├── model-training-evaluation.md
│   └── feature-engineering.md
│
├── sagemaker/                         # Amazon SageMaker
│   ├── sagemaker.md                   # SageMaker hub
│   ├── sagemaker-hyperparameters.md
│   ├── sagemaker-training.md
│   ├── sagemaker-jumpstart.md
│   └── sagemaker-clarify.md
│
├── aws-services/                      # AWS ML/AI Services
│   ├── aws-ml-algorithms.md
│   ├── aws-ai-services.md
│   ├── aws-generative-ai.md
│   └── data-services.md
│
├── mlops/                             # MLOps & Deployment
│   ├── mlops-deployment.md            # Deployment hub
│   ├── mlops-experiments.md
│   ├── mlops-cicd.md
│   └── mlops-monitoring.md
│
├── security/                          # Security
│   ├── security.md                    # Security hub
│   ├── security-encryption.md
│   └── security-network.md
│
├── guides/                            # Quick References
│   ├── study-guide.md                 # START HERE!
│   ├── cheat-sheet.md                 # Quick reference
│   └── TEMPLATE.md                    # Template
│
└── README.md                           # This file

🔗 External Resources

• AWS ML Associate Exam Guide
• AWS SageMaker Documentation
• AWS Machine Learning Blog
• AWS Skill Builder - Official practice exams

⚖️ License

MIT License - Feel free to use these notes for your own exam preparation!

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Good luck with your certification journey! 🎓

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