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Exam Information
Official specifications published by Amazon
Exam Format
170 min
65
750
Specialty
Registration
$300 USD
Pearson VUE or online proctoring
Validity
3 years
Retake the MLS-C01 exam before March 31, 2026; After retirement, certification remains valid for 3 years from date earned; No recertification path after March 31, 2026
MLS-C01 Exam Topics and Domains
MLS-C01 is organized into 4 weighted domains. Expect to work with Amazon SageMaker, Amazon EMR, AWS Glue, Amazon Bedrock, and more.
1
Data Engineering
20%
Create data repositories for ML
Identify data sourcesDetermine storage mediums
- Identify appropriate data sources for machine learning workloads
- Determine the most suitable storage medium for different types of ML data
Identify and implement a data ingestion solution
Data job styles and typesOrchestrate data ingestion pipelinesSchedule jobs
- Differentiate between batch and streaming data ingestion
- Design and implement data ingestion pipelines using AWS services
- Configure job scheduling for ML data workflows
Identify and implement a data transformation solution
Transform data in transit (ETL)Handle ML-specific data using MapReduce
- Design and implement ETL pipelines for ML data
- Apply MapReduce patterns to process large-scale ML datasets
2
Exploratory Data Analysis
24%
Sanitize and prepare data for modeling
Handle data quality issuesData formatting and normalizationLabeled data assessment
- Identify and remediate data quality issues
- Apply appropriate data formatting and normalization techniques
- Assess labeled data sufficiency and implement labeling solutions
Perform feature engineering
Feature extraction from diverse data sourcesFeature engineering concepts
- Extract meaningful features from text, images, speech, and time-series data
- Apply feature engineering techniques to improve model performance
- Reduce feature dimensionality while preserving information
Analyze and visualize data for ML
Create visualizationsInterpret descriptive statisticsPerform cluster analysis
- Create and interpret data visualizations for ML exploration
- Apply descriptive statistics to understand data distributions
- Perform and interpret cluster analysis
3
Modeling
36%
Frame business problems as ML problems
Determine when to use MLSupervised vs unsupervised learningSelect ML problem type
- Determine when ML is appropriate for a business problem
- Distinguish between supervised and unsupervised learning
- Map business problems to specific ML problem types
Select the appropriate model(s) for a given ML problem
Traditional ML algorithmsDeep learning modelsExpress intuition behind models
- Select appropriate ML algorithms based on problem type and data characteristics
- Understand when to use deep learning vs traditional ML
- Explain the intuition behind different model types
Train ML models
Data splitting and validationOptimization techniquesCompute resource selectionCompute platform selectionModel updates and retraining
- Implement proper data splitting and cross-validation
- Select appropriate optimization techniques for model training
- Choose compute resources and platforms based on workload requirements
- Design model update and retraining strategies
Perform hyperparameter optimization
Regularization techniquesCross-validation for hyperparameter tuningModel initializationNeural network hyperparametersTree-based model hyperparametersLinear model hyperparameters
- Apply regularization techniques to prevent overfitting
- Implement hyperparameter optimization strategies
- Tune neural network, tree-based, and linear model hyperparameters
Evaluate ML models
Detect overfitting and underfittingEvaluation metricsConfusion matrix interpretationModel evaluation strategiesModel comparisonCross-validation for evaluation
- Detect and mitigate overfitting and underfitting
- Select and interpret appropriate evaluation metrics
- Implement offline and online model evaluation strategies
- Compare models using multiple criteria beyond accuracy
4
Machine Learning Implementation and Operations
20%
Build ML solutions for performance, availability, scalability, resiliency, and fault tolerance
Logging and monitoringHigh availability deploymentInfrastructure as CodeAuto scaling and load balancingAWS best practices
- Implement logging and monitoring for ML workloads
- Design highly available and resilient ML solutions
- Create infrastructure as code for ML deployments
- Implement auto-scaling and load balancing for ML inference
Recommend and implement the appropriate ML services and features for a given problem
AWS AI/ML application servicesService quotas and limitsCustom vs built-in modelsInfrastructure and cost considerations
- Select appropriate AWS AI/ML services for business problems
- Understand and work within AWS service quotas
- Decide between custom and built-in models
- Optimize infrastructure costs for ML workloads
Apply basic AWS security practices to ML solutions
Identity and Access ManagementData securityNetwork security
- Implement IAM best practices for ML workloads
- Secure ML data with encryption and access controls
- Configure network security for ML solutions
Deploy and operationalize ML solutions
Model deploymentEndpoint interactionA/B testing and deployment strategiesRetraining pipelinesModel debugging and troubleshooting
- Deploy ML models using various inference options
- Implement A/B testing and safe deployment strategies
- Build automated retraining pipelines
- Monitor, debug, and troubleshoot deployed models
How do I earn this certification?
Passing MLS-C01 earns the AWS Certified Machine Learning - Specialty certification. It sits in the Machine Learning / Artificial Intelligence track.
Alternative Paths
- MLA-C01 - AWS Certified Machine Learning Engineer - AssociateThis is the recommended active certification for ML on AWS after MLS-C01 retires • Current active ML certification (Associate level), focuses on implementing ML workloads in production
- AIF-C01 - AWS Certified AI PractitionerEntry-level certification for those new to AI on AWS • Foundational AI certification covering AWS AI services and use cases
- DEA-C01 - AWS Certified Data Engineer - AssociateFocus on data pipelines and infrastructure supporting ML workloads
- DVA-C02 - AWS Certified Developer - AssociateApplication development skills useful for ML application integration
Practice with Precision
The PlanetCert Simulator mirrors the real exam environment with authentic questions and timed pressure.
How to study for this exam?
The most effective way to prepare for MLS-C01 is by using the PlanetCert Simulator to practice questions and review detailed explanations.
What's changed on this exam?
Current Status
- RETIRING
- Last content update: 2024
- Announcement date: 2024-12-12
Updates
- Amazon Bedrock Multiple foundation models Increasingly relevant for Domain 3 (Modeling) - understanding when to use foundation models vs custom models • Release date: 2024-11
- SageMaker Unified Studio Preview May appear in future exam updates - unified ML development environment • Release date: 2024-11
- SageMaker HyperPod Enhanced resilience Relevant for Domain 3 (Training) and Domain 4 (Implementation) - distributed training and fault tolerance • Release date: 2024-10
- SageMaker Model Monitor Continuous updates Critical for Domain 4 (MLOps) - model drift detection and monitoring • Release date: 2024
- Amazon Q Expanded capabilities Relevant for Domain 4 (ML Services) - understanding AWS AI application services • Release date: 2024-08
Who should take this exam?
- 2+ years of experience developing, architecting, and running ML/deep learning workloads in the AWS Cloud
- Ability to express the intuition behind basic ML algorithms
- Experience performing basic hyperparameter optimization
- Experience with ML and deep learning frameworks
- Ability to follow model-training best practices
- Ability to follow deployment and operational best practices