In the age of artificial intelligence and data-driven decision-making, machine learning (ML) has become a critical component in shaping industries, products, and services. Cloud platforms like Amazon Web Services (AWS) provide robust ML services and infrastructure to support the entire machine learning lifecycle—from data collection to model deployment. If you’re a data scientist, machine learning engineer, or developer working with ML models on AWS, the MLS-C01: AWS Certified Machine Learning – Specialty certification is one of the most valuable credentials you can earn.
This in-depth guide will walk you through everything you need to know about the MLS-C01 certification, including its scope, prerequisites, exam format, key topics, benefits, and preparation strategies.
What is the MLS-C01 Certification?
The MLS-C01 is the exam code for the AWS Certified Machine Learning – Specialty certification. This certification validates a candidate’s ability to build, train, tune, and deploy machine learning models on AWS. It focuses on the machine learning lifecycle and services provided by AWS such as SageMaker, Lambda, S3, and Glue.
The certification is ideal for professionals who work with data and ML pipelines and need to demonstrate their expertise in deploying scalable ML solutions using AWS tools.
Who Should Take MLS-C01?
The certification is intended for individuals in roles such as:
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Machine Learning Engineers
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Data Scientists
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Data Engineers
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AI/ML Developers
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Solutions Architects working on ML-based solutions
Candidates should ideally have at least 1–2 years of experience in developing, architecting, and running ML workloads on AWS.
Exam Overview
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Exam Name: AWS Certified Machine Learning – Specialty
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Exam Code: MLS-C01
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Format: Multiple choice and multiple response
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Duration: 180 minutes
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Number of Questions: 65
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Passing Score: AWS does not publish exact scores, but typically around 70% is required
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Cost: $300 USD
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Delivery: Pearson VUE or PSI testing centers (online or in-person)
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Language: English, Japanese, Korean, Simplified Chinese
Domains Covered in MLS-C01 Exam
The exam is divided into four domains, each focusing on different aspects of the machine learning lifecycle and AWS tools.
1. Data Engineering (20%)
This domain focuses on the collection, transformation, and storage of data for ML use.
Key topics include:
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Data ingestion using Kinesis, S3, DynamoDB, and RDS
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Data cleaning and transformation using AWS Glue, EMR, and Athena
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Understanding how to structure, partition, and optimize data storage for ML training
2. Exploratory Data Analysis (24%)
This domain assesses your ability to understand, visualize, and process raw data into usable insights.
Key topics:
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Feature engineering and selection
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Statistical analysis and data profiling
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Handling missing or imbalanced data
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Using Jupyter Notebooks, Pandas, and Matplotlib in SageMaker
3. Modeling (36%)
This is the most important domain, as it assesses your ability to build, train, and evaluate ML models.
Key topics:
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Supervised and unsupervised learning
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Model evaluation metrics (precision, recall, ROC AUC)
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Hyperparameter tuning with SageMaker
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Training ML models using SageMaker built-in algorithms, custom containers, and TensorFlow/PyTorch
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Model optimization for cost, performance, and accuracy
4. Machine Learning Implementation and Operations (20%)
Focuses on the deployment, automation, monitoring, and maintenance of ML solutions.
Key topics:
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Model deployment with SageMaker Endpoints, Lambda, and ECS
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CI/CD for ML workflows using CodePipeline, CodeBuild
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Monitoring and logging with CloudWatch, SageMaker Model Monitor
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Model versioning, rollback, and drift detection
Why Earn the MLS-C01 Certification?
✅ Demonstrates Expert-Level ML Skills
The certification shows employers and clients that you possess a strong understanding of the ML lifecycle and can work effectively with AWS services to deploy solutions at scale.
✅ Boosts Your Resume and Credibility
AWS certifications are globally recognized. Earning MLS-C01 validates your specialized skills, making you stand out in job interviews and internal promotions.
✅ Increases Salary Potential
Certified AWS professionals, particularly in machine learning, often earn 15–25% more than their non-certified counterparts. According to surveys, the MLS-C01 is one of the highest-paying AWS certifications.
✅ Expands Career Opportunities
Roles such as ML Engineer, Data Scientist, AI Solutions Architect, and Applied Scientist often list AWS ML certification as a preferred qualification.
✅ Keeps You Current
The MLS-C01 forces you to stay updated with the latest AWS services and best practices in machine learning. It’s a great motivator to stay sharp and informed in this rapidly evolving field.
How to Prepare for the MLS-C01 Exam
Here’s a step-by-step strategy to help you succeed:
1. Review the Official Exam Guide
AWS provides a detailed exam guide outlining each domain, objectives, and recommended experience. Use it as your roadmap.
2. Take Official AWS Training
AWS offers self-paced and instructor-led courses such as:
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“Machine Learning on AWS”
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“The Machine Learning Pipeline on AWS”
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“Practical Data Science with Amazon SageMaker”
These courses are available via AWS Skill Builder or Coursera.
3. Gain Hands-On Experience
Hands-on practice is critical. Work with:
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Amazon SageMaker (build, train, deploy models)
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AWS Glue and Athena (for ETL tasks)
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CloudWatch, S3, Lambda, and Step Functions (for automation and monitoring)
Use AWS Free Tier or SageMaker Studio Lab for cost-effective learning.
4. Use Practice Exams
Practice exams help identify your weak spots. Trusted sources include:
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Whizlabs
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Tutorials Dojo
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Udemy practice exams
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ExamPro
5. Read AWS Whitepapers
Key whitepapers include:
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AWS Machine Learning Lens
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AWS Well-Architected Framework
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Amazon SageMaker Developer Guide
These documents offer insight into AWS best practices.
6. Join AWS Communities
Participate in forums and communities:
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AWS re:Post
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r/AWS Certifications on Reddit
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LinkedIn study groups
These communities provide support, tips, and motivation from others on the same journey.
Tips for Exam Day
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Time Management: You’ll have 180 minutes for 65 questions. Don’t dwell too long on one question.
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Eliminate Wrong Answers: Use the process of elimination to increase your chances when unsure.
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Understand AWS Terminology: Questions will use AWS-specific terminology—ensure you’re comfortable with all service names and abbreviations.
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Mark and Review: Use the “mark for review” option to revisit tough questions at the end.
Boost your machine learning career with the MLS-C01 AWS Certified Machine Learning – Specialty exam and validate your expertise in building ML solutions on AWS.
