A structured learning curriculum covering all major objectives of the AWS Certified Machine Learning Specialty exam, with chapters organized from fundamentals to advanced topics.
This guide works best as a loop: read a chapter, test yourself with practice questions, look up unfamiliar terms in the glossary, then move to the next chapter.
15 chapters covering every exam objective. Each chapter includes key concepts, exam tips, common traps, comparison tables, and a 5-question quiz at the end.
Start Chapter 1Free timed and untimed practice with instant feedback and full explanations. Pick 10–120 questions per session. Filter by domain to drill your weak areas.
Go to practice testEvery MLS-C01term defined and searchable. Use it when a chapter mentions a concept you haven't seen before or want a quick refresher on.
Browse glossaryExam blueprint, domain weights, passing score, duration, cost, and registration links. Start here if you're new to this certification.
View exam guideMachine Learning Overview and Core Concepts
Objective 1.1 · Define machine learning and deep learning concepts and their applications
Data Engineering Foundations for Machine Learning
Objective 2.1 · Identify data sources and data ingestion methods
Data Preparation and Transformation
Objective 2.2 · Perform data cleaning, transformation, and feature engineering
AWS Data Stores and Ingestion Pipelines
Objective 2.3 · Select and implement appropriate AWS data stores for ML workflows
Exploratory Data Analysis and Visualization
Objective 3.1 · Explore and visualize data to identify patterns, anomalies, and relationships
Feature Engineering, Encoding, and Selection
Objective 3.2 · Apply feature engineering techniques and select relevant features
Data Quality, Missing Values, and Bias Detection
Objective 3.3 · Analyze data quality issues and detect potential bias
Modeling Fundamentals: Algorithms and Problem Types
Objective 1.2 · Identify appropriate ML algorithms for different problem types
Training, Tuning, and Evaluating Models
Objective 1.3 · Train, tune, and evaluate ML models using AWS services
Amazon SageMaker Built-in Algorithms and AutoML
Objective 1.4 · Use Amazon SageMaker built-in algorithms and AutoML capabilities
Model Deployment and Inference on SageMaker
Objective 4.1 · Deploy models to production and manage inference endpoints
ML Pipelines and MLOps with SageMaker
Objective 4.2 · Build and automate ML pipelines for continuous integration and delivery
Monitoring and Remediating Models in Production
Objective 4.3 · Monitor model performance, detect drift, and implement remediation
Security, Compliance, and Governance for ML Workloads
Objective 4.4 · Implement security, compliance, and governance for ML solutions
Cost Optimization and Performance Tuning for ML
Objective 4.5 · Optimize ML costs and performance across the ML lifecycle
Free MLS-C01 practice questions with full explanations. Test what you learn chapter by chapter.
MLS-C01 Practice Questions