Strategies for excelling across all four exam domains of the AWS Certified Machine Learning – Specialty certification

As organizations rapidly adopt AI solutions, the demand for machine learning (ML) professionals continues to grow. According to the World Economic Forum Future of Jobs Report 2025, demand for AI and ML specialists is expected to grow by more than 80% by 2030.

To address this growing demand, Amazon Web Services (AWS) offers a comprehensive certification pathway that helps professionals build and validate their ML knowledge and skills in building, training, tuning, and deploying ML models.

In this post, we explain how to prepare for the AWS Certified Machine Learning – Specialty certification, whether you’re starting from scratch or building upon existing AWS Certifications. We share the prerequisites and guidance to help you get ready for this certification and demonstrate your expertise in building ML solutions with AWS.

The four domains: A comprehensive study guide

The AWS Certified Machine Learning – Specialty Exam Guide provides a blueprint of the certification’s structure, detailing the four critical domains and their specific task statements. This document serves as the definitive roadmap for candidates, outlining the key knowledge and skills required to demonstrate proficiency in ML with AWS.

The four domains are as follows:

Domain 1: Data engineering (20% of scored content)
Domain 2: Exploratory data analysis (24% of scored content)
Domain 3: Modeling (36% of scored content)
Domain 4: Machine learning implementation and operations (20% of scored content)

Domain 1: Data engineering

The data engineering domain focuses on critical skills in data management and transformation for ML workflows. Candidates must demonstrate proficiency in creating data repositories, identifying data sources, and implementing ingestion solutions using AWS services. The domain covers essential skills in data transformation, including ETL processes and handling data pipelines necessary for developing effective machine learning solutions.

AWS Skill Builder Exam Prep Plan: AWS Certified Machine Learning – Specialty provides you with study material for each domain plus official practice question sets. The Domain 1 Review: AWS Certified Machine Learning – Specialty course offers expert-led video instruction that aligns AWS services with key learning objectives. For additional learning, explore the Data Engineer course, covering Domain 1 concepts and AWS services.

Domain 2: Exploratory data analysis

The exploratory data analysis domain focusses on critical skills for transforming raw data into ML-ready insights. Candidates must demonstrate proficiency in data preparation, feature engineering, and techniques for uncovering hidden patterns in datasets. The domain assesses your readiness to handle data preprocessing, normalization, and feature selection essential for improving ML model performance.

The Domain 2 Review: AWS Certified Machine Learning – Specialty course offers expert-led video instruction that aligns AWS services with key data analysis learning objectives. The Digital Classroom – Practical Data Science with Amazon SageMaker course has modules and labs that cover data preparation and transformation techniques.

Domain 3: Modeling

Modeling comprises the largest segment of the AWS Certified Machine Learning – Specialty certification, covering the comprehensive modeling lifecycle across different learning paradigms. Candidates must demonstrate understanding of ML algorithms, model training techniques, and evaluation metrics. The domain challenges professionals to master critical skills in algorithm selection, model training, and performance evaluation across various ML scenarios.

The Domain 3 Review: AWS Certified Machine Learning – Specialty course offers expert-led video instruction that aligns AWS services with key ML modeling techniques. Amazon SageMaker AI Getting Started is a complementary resource for hands-on learning.

Domain 4: Machine learning implementation and operations

Domain 4 focuses on translating ML models into production-ready solutions. Candidates must demonstrate expertise in deployment strategies, ML operations (MLOps) lifecycle management, and model monitoring. The domain challenges professionals to master critical skills in implementing ML solutions, optimizing infrastructure, and ensuring effective model performance in production environments.

The Domain 4 Review: AWS Certified Machine Learning – Specialty course offers expert-led video instruction that aligns AWS services with key MLOps and deployment strategies. To learn more about implementation and operational aspects of ML, refer to Digital Classroom – MLOps Engineering on AWS.

Traditional path to ML specialty

Starting your journey to successfully completing the AWS Certified Machine Learning – Specialty certification requires a solid foundation. You should have a basic understanding of Python programming and familiarity with fundamental statistical concepts and ML principles. Becoming an AWS ML specialist can follow a structured progression, but the certification is achievable through any learning path that builds the required expertise.

For candidates who wish to follow a structured path, these are the steps to take:

Step 1: AWS Certified AI Practitioner (CLF-C02)

Perfect for beginners and business professionals, this entry-level certification focuses on practical AI knowledge, foundational concepts, and introduces core AWS AI tools such as Amazon SageMaker, Amazon Comprehend, and Amazon Lex.

Step 2: AWS Certified Machine Learning Engineer – Associate (MLA-C01)

This mid-level certification focuses on the complete ML lifecycle and is designed for practitioners who implement, deploy, and maintain ML solutions on AWS. It covers everything from data preparation and model training to workflow orchestration and monitoring.

Step 3: AWS Certified Machine Learning – Specialty (MLS-C01)

For experienced professionals with at least 2 years of ML experience, this advanced certification validates expertise in data engineering, analytics, and model optimization.

This progression helps you develop both breadth and depth in cloud computing knowledge while building specialized expertise in ML technologies—skills increasingly sought after as organizations continue to leverage AI/ML for competitive advantage.

For more information about mapping your AI/ML career journey, including preparation resources and strategic guidance, visit the Mapping your AI/ML career journey in the AWS Training and Certification Blog.

Building from AWS Certified Machine Learning Engineer – Associate or AWS Data Engineer – Associate certification

Whether you’re coming from a data engineering or ML background, the ML specialty certification requires mastery of the complete ML lifecycle. Both paths converge in the ML specialty’s emphasis on:

End-to-end ML pipelines – Understanding how data flows from ingestion through preprocessing, training, evaluation, and deployment
Production-grade ML systems – Building scalable and secure ML solutions
Advanced feature engineering – Creating features that improve model performance
MLOps practices – Implementing continuous integration and deployment (CI/CD) for ML models

Preparing for ML specialty with data engineering foundation

As a certified AWS Data Engineer – Associate, your expertise in services such as AWS Glue, Amazon EMR, and data storage options provides a solid base for the Data Engineering and Exploratory Data Analysis domain of the AWS Certified Machine Learning – Specialty exam. To successfully bridge the gap, focus on expanding your knowledge of ML-specific data preparation techniques, including feature engineering, data cleaning for ML workloads, and understanding how data quality impacts model performance.

Pay special attention to the data preparation capabilities of SageMaker, which might be new territory compared to your existing data engineering toolkit. Domain 3 will present the most new information because you’ll need to develop expertise in selecting appropriate algorithms, hyperparameter tuning, and model evaluation metrics, topics that weren’t covered extensively in the data engineering certification.

Preparing for ML specialty with ML engineering foundation

As an AWS Certified Machine Learning Engineer – Associate, you’re already familiar with SageMaker and the fundamentals of building, training, and deploying ML models. Your strength in the modeling domain gives you a head start on the largest portion of the ML specialty exam. However, to excel in the ML specialty certification, you’ll need to deepen your understanding of the data engineering aspects that support sophisticated ML workflows.

Focus on expanding your knowledge of large-scale data processing systems such as Amazon EMR and AWS Glue that weren’t emphasized in your associate certification. The ML specialty exam demands a more advanced understanding of exploratory data analysis, including statistical methods and visualization techniques for uncovering patterns in large datasets. It requires expertise in optimizing models for production environments and implementing sophisticated MLOps practices. You should also strengthen your knowledge of operational aspects such as model monitoring and implementing ML pipelines at scale.

Next steps on your journey

AWS offers a variety of training options designed to accommodate different learning styles, including AWS Skill Builder Exam Prep Plan, hands-on labs and interactive games, AWS Training Live for expert-led cloud training, and free online and in-person training events. By using resources such as AWS Skill Builder, AWS Educate, and Udemy Business Leadership Academy cohort programs, you can accelerate your learning in the fast-moving discipline of AI/ML.

Skill Builder provides free Official Practice Question Sets to understand the exam format. These 20-question sets, developed by AWS, demonstrate the certification exam style.

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