AWS Certified Machine Learning Specialty (MLS-C01) Study Guide: Complete Learning Path

Preparing for the AWS Certified Machine Learning - Specialty (MLS-C01) exam? This comprehensive study guide maps our three-part SageMaker tutorial series to all exam domains, providing you with practical, hands-on preparation for certification success.

📋 Exam Overview

AWS Certified Machine Learning - Specialty (MLS-C01)

• Cost: $300 USD
• Format: 65 questions, multiple choice and multiple response
• Time: 170 minutes (2 hours 50 minutes)
• Passing Score: 750/1000 (75%)
• Validity: 3 years
• Prerequisites: None, but ML experience recommended

🎯 Exam Domains & Weightings

📚 Complete Learning Path: Our Three-Post Series

Phase 1: Foundation Building

Beginner’s SageMaker Guide

Domain Coverage:

• Domain 1 (20%): Basic data engineering concepts, AWS service integration
• Domain 4 (20%): AWS ML service selection, basic security practices

Key Topics:

• SageMaker Studio setup and configuration
• IAM roles and permissions for ML workloads
• Cost optimization strategies
• Basic model training workflows

Exam Relevance: Provides foundational AWS knowledge required for all MLS-C01 questions.

Phase 2: Algorithm Mastery & Computer Vision

Image Recognition Deep Dive

Domain Coverage:

• Domain 2 (24%): Advanced feature engineering for images
• Domain 3 (36%): Algorithm selection, model training, evaluation
• Domain 4 (20%): AWS Rekognition vs SageMaker implementation

Key Topics:

• Traditional ML (SVM, Random Forest) vs Deep Learning (CNN)
• Transfer learning and pre-trained models
• AWS Rekognition managed service
• Production deployment strategies

Exam Relevance: Computer vision questions frequently appear. Understanding service selection is crucial.

Phase 3: Professional ML Engineering

Advanced Professional ML Techniques

Domain Coverage:

• Domain 2 (24%): Advanced EDA, feature engineering, data visualization
• Domain 3 (36%): Cross-validation, hyperparameter tuning, model evaluation
• Domain 4 (20%): Production deployment, monitoring, operationalization

Key Topics:

• Real dataset analysis (California Housing)
• Professional ML workflows and best practices
• Model validation and error analysis
• Production-ready code patterns

Exam Relevance: Covers core “Evaluate machine learning models” and “Perform hyperparameter optimization” objectives.

🗺️ Detailed Domain Mapping

Domain 1: Data Engineering (20%)

Exam Objectives:

• Create data repositories for ML
• Identify and implement data ingestion solutions
• Identify and implement data transformation solutions

Our Coverage:

• Beginner Guide: S3 data storage, basic ingestion patterns
• Practical Examples: Terraform automation for data infrastructure
• AWS Services: Glue, Kinesis, Data Pipeline concepts

Study Focus: Understand data lake vs warehouse architectures, ETL vs ELT patterns.

Domain 2: Exploratory Data Analysis (24%)

Exam Objectives:

• Sanitize and prepare data for modeling
• Perform feature engineering
• Analyze and visualize data for ML

Our Coverage:

• Professional ML Post: Complete EDA workflow with real data
• Image Recognition Post: Computer vision feature engineering
• Techniques: Missing data handling, outlier detection, correlation analysis

Study Focus: Statistical methods, feature selection algorithms, data quality assessment.

Domain 3: Modeling (36%) - Most Important Domain

Exam Objectives:

• Frame business problems as ML problems
• Select appropriate model(s) for given ML problems
• Train ML models
• Perform hyperparameter optimization
• Evaluate ML models

Our Coverage:

• Image Recognition Post: Algorithm selection framework
• Professional ML Post: Cross-validation, hyperparameter tuning
• Beginner Guide: Basic model training concepts

Study Focus: Algorithm selection criteria, evaluation metrics, validation techniques.

Domain 4: ML Implementation and Operations (20%)

Exam Objectives:

• Build ML solutions for performance, availability, scalability, resiliency, and fault tolerance
• Recommend and implement appropriate ML services and features
• Apply basic AWS security practices to ML solutions
• Deploy and operationalize ML solutions

Our Coverage:

• Beginner Guide: Cost optimization, security basics
• Image Recognition Post: Rekognition vs SageMaker deployment
• Professional ML Post: Production deployment patterns

Study Focus: Service selection, scalability patterns, security best practices.

🧠 Exam Preparation Strategy

Step 1: Build Foundations (1-2 weeks)

1. Read AWS MLS-C01 exam guide thoroughly
2. Complete our Beginner’s SageMaker Guide
3. Set up AWS account and practice basic SageMaker operations
4. Review AWS ML service documentation

Step 2: Deep Dive into Algorithms (2-3 weeks)

1. Study our Image Recognition Guide
2. Practice algorithm selection for different problem types
3. Implement computer vision projects on SageMaker
4. Compare managed services (Rekognition) vs custom models

Step 3: Professional Techniques (2-3 weeks)

1. Master our Professional ML Guide
2. Practice EDA, feature engineering, and model evaluation
3. Implement end-to-end ML pipelines
4. Focus on production deployment and monitoring

Step 4: Practice Exams & Review (1-2 weeks)

1. Take AWS practice exams
2. Review weak areas
3. Practice hands-on labs
4. Schedule and take the exam

📋 Key Exam Topics & Our Coverage

🎯 Exam Day Tips

Question Types:

• Multiple Choice: Single correct answer
• Multiple Response: Multiple correct answers (clearly indicated)
• Scenario-based: Real-world ML problems

Time Management:

• 65 questions in 170 minutes = ~2.6 minutes per question
• Flag difficult questions and return to them
• Don’t spend too much time on any single question

Common Pitfalls:

• Reading questions too quickly
• Not understanding AWS service limitations
• Confusing similar services (SageMaker vs Rekognition vs Comprehend)
• Missing security requirements

Scoring:

• Questions have different weightings
• Passing score is 750/1000 (75%)
• Results available immediately

🛠️ Additional Resources

Official AWS Resources:

• AWS MLS-C01 Exam Guide
• AWS Machine Learning Blog
• SageMaker Documentation

Practice & Hands-on:

• AWS SageMaker Examples
• AWS Deep Learning Containers
• AWS ML Certification Practice Exams

Community Resources:

• AWS Machine Learning Community
• Reddit r/aws
• AWS Blogs and Whitepapers

📊 Success Metrics

Track your progress:

• ✅ Complete all three tutorial posts
• ✅ Implement 3+ ML projects on SageMaker
• ✅ Score 80%+ on practice exams
• ✅ Understand all exam domains thoroughly
• ✅ Practice time management (65 questions in 170 minutes)

🎓 Certification Benefits

Why get AWS MLS-C01 certified?

• Career Advancement: High-demand ML specialty certification
• Salary Increase: 10-20% salary premium for certified professionals
• Industry Recognition: Globally recognized AWS certification
• Skill Validation: Demonstrates hands-on ML expertise
• Job Opportunities: Opens doors to ML engineering and data science roles

🚀 Next Steps

Ready to start your MLS-C01 journey?

1. Begin with Foundations: SageMaker for Beginners
2. Master Algorithms: Image Recognition Guide
3. Professional Excellence: Advanced ML Techniques
4. Practice & Certify: Take the exam and join the AWS ML certified community!

Remember: The MLS-C01 exam tests practical ML knowledge, not just theory. Our hands-on tutorials provide the perfect preparation foundation!

This study guide provides a complete roadmap for AWS MLS-C01 certification success. Each of our tutorial posts is specifically designed to build the practical skills tested on the exam. 🎯📚