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Exam Information
Official specifications published by Amazon
Exam Format
130 min
65
720
Associate
Registration
$150 USD
Pearson VUE or online proctoring
[object Object], [object Object], [object Object], [object Object]
Validity
3 years
Pass the current version of AWS Certified Machine Learning Engineer - Associate exam; Pass a higher-level AWS Certification exam (e.g., AWS Certified Machine Learning - Specialty); Complete AWS Professional Development courses and earn continuing education credits
MLA-C01 Exam Topics and Domains
MLA-C01 is organized into 4 weighted domains. Expect to work with SageMaker Clarify, CloudWatch Metrics, SageMaker Experiments, SageMaker Model Monitor, and more.
1
Data Preparation for Machine Learning (ML)
28%
Ingest and store data
Data ingestion from various sourcesData storage strategies
- Identify appropriate data ingestion mechanisms for various data sources
- Select optimal storage solutions based on ML workload requirements
- Implement data partitioning strategies for efficient access
- Configure streaming data pipelines for real-time ML applications
Transform data and perform feature engineering
Data cleaning and transformationFeature engineering techniquesData splitting strategies
- Apply appropriate data cleaning techniques for ML datasets
- Implement feature engineering methods to improve model performance
- Use SageMaker Data Wrangler and Glue for data transformation
- Design feature engineering pipelines for production ML systems
Ensure data integrity and prepare data for modeling
Bias detection and mitigationClass imbalance handlingData security and compliance
- Detect and address bias in ML training data
- Implement strategies to handle class imbalance
- Ensure data security and compliance with regulations
- Apply data validation techniques before model training
- Configure SageMaker Clarify for bias detection
- Implement SMOTE for minority class oversampling
- Set up encryption for S3 data with KMS
- Use Macie to detect PII in datasets
2
ML Model Development
26%
Choose a modeling approach
Algorithm selection for use casesAWS AI services vs custom MLModel interpretability requirements
- Select appropriate ML algorithms for different use cases
- Evaluate when to use AWS AI services vs custom models
- Understand model interpretability requirements and tools
- Leverage SageMaker JumpStart for rapid prototyping
Train and refine models
Training infrastructure and optimizationHyperparameter tuningRegularization and overfitting preventionModel versioning and experiment tracking
- Configure and optimize SageMaker training jobs
- Implement hyperparameter tuning strategies
- Apply regularization to prevent overfitting
- Track experiments and manage model versions
Analyze model performance
Classification metricsRegression metricsModel validation and testingModel explainability analysis
- Select and interpret appropriate evaluation metrics
- Analyze model performance using SageMaker tools
- Detect overfitting and underfitting
- Use SageMaker Clarify for model insights and bias detection
- Generate and interpret confusion matrices
- Calculate and compare F1, precision, recall
- Use SageMaker Clarify for explainability
- Set up CloudWatch dashboards for model metrics
3
Deployment and Orchestration of ML Workflows
22%
Select deployment infrastructure
Inference patterns and endpoint typesCompute resource provisioningEdge deployment
- Select appropriate deployment infrastructure for ML workloads
- Understand different SageMaker endpoint types and use cases
- Optimize compute resources for cost and performance
- Deploy models to edge devices when appropriate
Create and script infrastructure
Infrastructure as Code (IaC)Auto-scaling configurationsOn-demand vs provisioned resources
- Implement Infrastructure as Code for ML workflows
- Configure auto-scaling for SageMaker endpoints
- Optimize resource provisioning for cost and performance
- Use CloudFormation/CDK to manage ML infrastructure
Set up CI/CD pipelines
MLOps pipeline componentsDeployment strategiesVersion control and artifact managementAutomated orchestration
- Design and implement CI/CD pipelines for ML
- Configure deployment strategies (blue/green, canary)
- Automate ML workflows using SageMaker Pipelines
- Integrate version control with ML workflows
- Create SageMaker Pipelines for end-to-end workflows
- Configure blue/green deployments
- Set up EventBridge rules for automated retraining
- Implement model approval workflows
4
ML Solution Monitoring, Maintenance, and Security
24%
Monitor model inference
Model drift detectionData quality monitoringPrediction quality monitoringA/B testing and experimentation
- Implement model monitoring with SageMaker Model Monitor
- Detect and respond to data and model drift
- Set up A/B testing for model comparison
- Monitor data quality in production ML systems
Monitor and optimize infrastructure and costs
Performance metrics monitoringCost optimization strategiesResource optimization
- Monitor ML infrastructure performance with CloudWatch
- Implement cost optimization strategies for ML workloads
- Optimize resource utilization for inference endpoints
- Set up alerting for performance and cost anomalies
Secure AWS resources
IAM and access controlNetwork securityData encryption and key managementCI/CD security best practicesCompliance and governance
- Implement security best practices for SageMaker
- Configure IAM roles and policies for ML workloads
- Secure network access for ML infrastructure
- Ensure compliance and governance for ML systems
- Configure SageMaker in VPC with private endpoints
- Create IAM roles with least privilege for SageMaker
- Enable encryption for S3 data and SageMaker resources
- Set up CloudTrail logging for ML operations
How do I earn this certification?
Passing MLA-C01 earns the AWS Certified Machine Learning Engineer - Associate certification. It sits in the Machine Learning & AI track.
Current Level Exams
Next Level Options
- MLS-C01 - AWS Certified Machine Learning - SpecialtyNatural progression - deeper ML knowledge, data science focus, architecture design
- SAP-C02 - AWS Certified Solutions Architect - ProfessionalAdvanced AWS architecture including ML workloads
- DOP-C02 - AWS Certified DevOps Engineer - ProfessionalAdvanced CI/CD and automation for ML pipelines
Alternative Paths
- DAS-C01 - AWS Certified Data Analytics - SpecialtyStrong overlap in data preparation, ETL, and analytics services used in ML
- DBS-C01 - AWS Certified Database - SpecialtyDatabase knowledge supports ML data storage strategies
- ANS-C01 - AWS Certified Advanced Networking - SpecialtyNetwork design for secure ML infrastructure
- SCS-C02 - AWS Certified Security - SpecialtySecurity best practices for ML workloads
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 MLA-C01 is by using the PlanetCert Simulator to practice questions and review detailed explanations.
What's changed on this exam?
Current Status
- ACTIVE
- Last content update: 2024
- Announcement date: 2024
Updates
- Amazon SageMaker Unified Studio 1.0 May be included in future exam updates - monitor AWS announcements • Release date: 2024-11
- SageMaker Pipelines Latest Core exam topic - MLOps and CI/CD workflows heavily tested • Release date: Continuous updates
- Amazon Bedrock Latest Foundation models and when to use vs custom models - tested in Domain 2 • Release date: 2023-ongoing
- SageMaker Model Monitor Latest Core exam topic - model drift detection in Domain 4 • Release date: Continuous updates
- SageMaker Clarify Latest Core exam topic - bias detection and model explainability in Domains 1, 2, 4 • Release date: Continuous updates
Who should take this exam?
- 1+ years of hands-on experience with Amazon SageMaker
- 1+ years of experience in ML engineering or related field
- Understanding of common ML algorithms and use cases
- Data engineering fundamentals
- Software development best practices
- CI/CD pipeline experience
- AWS foundational knowledge