AWS Certified Machine Learning Engineer - Associate (MLA-C01)
Complete MLA-C01 exam guide covering machine learning data preparation, feature engineering, model development, training, deployment, MLOps, monitoring, maintenance, security and AWS machine learning services.
View Official AWS Exam Guide →AWS Certified Machine Learning Engineer - Associate (MLA-C01): Complete Guide
The AWS Certified Machine Learning Engineer - Associate (MLA-C01) certification validates technical skills for implementing, deploying, operationalizing and maintaining machine learning solutions and pipelines using AWS Cloud.
The certification focuses on practical machine learning engineering activities such as preparing data, developing and evaluating ML models, deploying models, automating ML workflows, monitoring ML systems and applying security and operational best practices.
The AWS target candidate profile includes professionals with hands-on experience using Amazon SageMaker and other AWS machine learning services. Relevant roles include ML engineers, MLOps engineers, data engineers, DevOps engineers, backend developers and data scientists.
MLA-C01 Exam at a Glance
AWS Certified Machine Learning Engineer - Associate
MLA-C01
130 minutes
65 questions
USD 150
AWS currently lists MLA-C01 as a 130-minute, 65-question Associate-level certification exam with a USD 150 exam fee. The exam can include multiple choice, multiple response, ordering and matching question types.
Who Should Take the AWS MLA-C01 Exam?
AWS Certified Machine Learning Engineer - Associate is designed for professionals who implement and operate machine learning workloads and pipelines on AWS.
- Machine Learning Engineers
- MLOps Engineers
- Data Engineers
- Data Scientists
- DevOps Engineers
- Backend Software Developers
- Cloud Engineers working with ML workloads
- Professionals moving into AWS machine learning engineering
- AI/ML professionals working with Amazon SageMaker
AWS identifies at least 1 year of experience using Amazon SageMaker and other AWS services for ML engineering as part of the target candidate profile.
AWS MLA-C01 Exam Domains
The MLA-C01 exam is divided into four major content domains. Understanding the domain weightings is important when creating an AWS Machine Learning Engineer Associate study plan.
Domain 1
Data Preparation for Machine Learning
Ingest, transform, validate and prepare data for ML modeling.
Domain 2
ML Model Development
Develop, train, tune, evaluate and manage ML models.
Domain 3
Deployment & Orchestration
Deploy models and automate ML workflows and pipelines.
Domain 4
Monitoring, Maintenance & Security
Monitor, maintain, troubleshoot and secure ML solutions.
AWS currently assigns 28%, 26%, 22% and 24% of scored content to Domains 1 through 4 respectively.
1. Data Preparation for Machine Learning
Data preparation is the largest MLA-C01 exam domain. Candidates should understand how to ingest, store, transform, validate and prepare datasets for machine learning.
Important MLA-C01 Topics
- Data ingestion
- Data storage
- Data formats
- Amazon S3
- Amazon EFS
- Amazon FSx
- Amazon RDS
- Amazon DynamoDB
- Amazon Kinesis
- Apache Kafka
- Apache Flink
- Data transformation
- Feature engineering
- Data cleaning
- Data validation
- Data quality
- Data labeling
- Data bias
- Data anonymization and masking
- PII and data privacy
AWS specifically covers data formats such as Parquet, JSON, CSV, ORC and Avro, along with data ingestion, transformation, feature engineering, validation and preparation activities. Relevant AWS tools include Amazon SageMaker Data Wrangler, SageMaker Feature Store, AWS Glue, AWS Glue DataBrew and SageMaker Ground Truth.
2. ML Model Development
The second MLA-C01 domain focuses on developing machine learning models, selecting appropriate modeling approaches, training models, tuning hyperparameters and evaluating model performance.
Key Topics
- ML algorithms and use cases
- Model selection
- Model training
- Training datasets
- Validation datasets
- Test datasets
- Hyperparameter tuning
- Model evaluation
- Model performance metrics
- Overfitting and underfitting
- Model versioning
- Model artifacts
- Experiment tracking
- Feature selection
- Model optimization
Candidates should understand how to choose appropriate machine learning approaches, train models, tune hyperparameters, analyze model performance and manage model versions throughout the ML development lifecycle.
3. Deployment and Orchestration of ML Workflows
Machine learning engineering does not stop after model training. MLA-C01 also tests practical skills related to deploying models, selecting infrastructure, configuring endpoints, scaling workloads and automating ML workflows.
Key Topics
- Model deployment
- Amazon SageMaker endpoints
- Real-time inference
- Batch inference
- Serverless inference
- Inference infrastructure
- Auto scaling
- Model hosting
- ML pipelines
- CI/CD for machine learning
- Infrastructure as Code
- AWS CloudFormation
- AWS CodePipeline
- AWS CodeBuild
- Workflow automation
- Pipeline orchestration
Candidates should understand how deployment requirements affect infrastructure, endpoint configuration, scaling, performance and cost. Automation and CI/CD are also important parts of operationalizing ML workflows.
4. ML Solution Monitoring, Maintenance and Security
Production machine learning systems need continuous monitoring and maintenance. MLA-C01 covers monitoring models, data and infrastructure while also applying AWS security and compliance best practices.
Key Topics
- Model monitoring
- Data monitoring
- Model drift
- Data drift
- Model performance monitoring
- Amazon CloudWatch
- CloudWatch Logs
- AWS CloudTrail
- Logging and troubleshooting
- Model maintenance
- IAM
- Least privilege access
- AWS KMS
- Encryption
- Data protection
- Security and compliance
ML engineers should be able to detect operational and model-related issues, monitor infrastructure and apply access controls, encryption and data protection mechanisms to ML workloads.
Important AWS Services for MLA-C01
The MLA-C01 exam covers AWS services relevant to machine learning engineering. Preparation should focus on understanding when and why to use each service, including its performance, scalability, security, operational and cost characteristics.
Amazon SageMaker AI, SageMaker Data Wrangler, SageMaker Feature Store, SageMaker Ground Truth, SageMaker Model Monitor
Amazon S3, Amazon EFS, Amazon FSx, Amazon RDS, Amazon DynamoDB, Amazon Redshift
AWS Glue, AWS Glue DataBrew, Amazon EMR, Amazon Kinesis, Apache Spark
Amazon EC2, AWS Lambda, Amazon ECS, Amazon EKS, AWS Fargate
AWS CodePipeline, AWS CodeBuild, AWS CodeDeploy, Amazon ECR, AWS CloudFormation
Amazon CloudWatch, AWS CloudTrail, AWS IAM, AWS KMS, AWS Secrets Manager, AWS Config
How to Prepare for AWS Certified Machine Learning Engineer - Associate
Step 1: Understand the MLA-C01 Exam Blueprint
Begin with the official AWS MLA-C01 exam guide and understand all four content domains, their weightings and the tasks covered under each domain.
Step 2: Learn Amazon SageMaker
Amazon SageMaker is central to the MLA-C01 target skill set. Study model training, deployment, inference, endpoints, monitoring, pipelines, feature engineering and model management.
Step 3: Build Strong Data Engineering Fundamentals
Learn Amazon S3, data formats, data ingestion, transformation, data quality, feature engineering, data partitioning and storage choices.
Step 4: Practice ML Model Development
Understand model training, validation, testing, model metrics, hyperparameter tuning, model selection and model version management.
Step 5: Learn ML Deployment and MLOps
Practice deploying models and building automated ML workflows. Study CI/CD, SageMaker pipelines, endpoint deployment, auto scaling and infrastructure as code.
Step 6: Study Monitoring and Security
Learn CloudWatch monitoring, logs, model and data monitoring, drift detection, IAM permissions, encryption, KMS and security best practices.
Step 7: Build Hands-On ML Projects
Create practical projects involving data preparation, model training, deployment, monitoring and automated workflows. Hands-on experience can help connect individual AWS services with real machine learning engineering requirements.
Step 8: Practice Exam Questions
Use practice questions to identify weak areas. Focus on understanding why a particular AWS service or architecture is appropriate rather than simply memorizing answers.
MLA-C01 Study Roadmap
Learn AWS fundamentals and machine learning concepts.
Master SageMaker and AWS data preparation services.
Practice model training, evaluation and tuning.
Learn deployment, MLOps and CI/CD workflows.
Practice monitoring, security and troubleshooting.
Take practice exams and review weak domains.
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AWS exam guides and certification requirements can be updated over time. Always check the latest official AWS MLA-C01 exam guide before scheduling your exam or finalizing your study plan.
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Send an Enquiry →Frequently Asked Questions About MLA-C01
What is AWS Certified Machine Learning Engineer - Associate?
AWS Certified Machine Learning Engineer - Associate is an AWS Associate-level certification focused on implementing, deploying, operationalizing and maintaining machine learning solutions and pipelines on AWS.
What is the AWS MLA-C01 exam?
MLA-C01 is the exam code for AWS Certified Machine Learning Engineer - Associate. AWS currently lists 65 questions and a 130-minute exam duration.
What are the MLA-C01 exam domains?
The four domains are Data Preparation for Machine Learning, ML Model Development, Deployment and Orchestration of ML Workflows, and ML Solution Monitoring, Maintenance and Security.
What AWS services should I study for MLA-C01?
Important areas include Amazon SageMaker, Amazon S3, AWS Glue, Amazon Kinesis, Amazon EFS, Amazon RDS, DynamoDB, AWS Lambda, Amazon CloudWatch, AWS IAM, AWS KMS, AWS CloudFormation and AWS CI/CD services. Candidates should also understand how these services support ML engineering workflows.
Is Amazon SageMaker important for MLA-C01?
Yes. SageMaker is a major part of the AWS machine learning engineering skill set. Candidates should understand data preparation, model training, deployment, inference, pipelines, monitoring and model management using SageMaker.
Is hands-on AWS machine learning practice important?
Yes. Hands-on practice with data preparation, model development, SageMaker, deployment, monitoring, MLOps and AWS security can make MLA-C01 preparation more practical and effective.
Start Your AWS Machine Learning Engineering Journey
Strengthen your AWS machine learning knowledge, practice real-world ML workflows and prepare strategically for the MLA-C01 certification.
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AWS Certified Machine Learning Engineer - Associate (MLA-C01)
Prepare for the AWS Certified Machine Learning Engineer - Associate (MLA-C01) certification and strengthen your knowledge of machine learning engineering, Amazon SageMaker, data preparation, feature engineering, model development, model deployment, MLOps, monitoring, troubleshooting and AWS security.
Whether you are preparing for the AWS MLA-C01 exam, beginning a career in machine learning engineering, moving into MLOps or improving your AWS AI/ML skills, Eduarn provides learning resources designed to support practical and career-focused learning.
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