AWS Certified Machine Learning Engineer - Associate Certification Guide 2026
Validates ability to build, deploy, and maintain machine learning solutions using AWS services. Covers data preparation, model development, deployment orchestration, and ML solution monitoring and security.
Build Production ML Systems on AWS
The ML engineering certification for practitioners who deploy models to production
Why This Certification Is Worth It
- First AWS certification focused on ML engineering (not just ML theory)
- ML Engineer is the fastest-growing and highest-paid tech role
- Master SageMaker, MLOps, and production ML deployment
- Complements ML Specialty with hands-on engineering focus
- High ROI: $25K-$40K salary increase vs $150 exam cost
- Opens doors to senior ML engineering and AI platform roles
Quick Navigation
What is AWS Certified Machine Learning Engineer - Associate?
The AWS Certified Machine Learning Engineer - Associate (MLA-C01) is a associate-level certification offered by Amazon Web Services (AWS).Validates ability to build, deploy, and maintain machine learning solutions using AWS services. Covers data preparation, model development, deployment orchestration, and ML solution monitoring and security.
Recommended Experience
1+ year experience with Amazon SageMaker and AWS ML services, plus 1+ year in a related role (data engineer, data scientist, or ML developer)
Who Should Take This Certification?
This certification is ideal for:
- Cloud practitioners with 1+ years of hands-on experience
- Solutions architects, developers, or DevOps engineers
- IT professionals looking to validate their cloud expertise
- Anyone looking to advance their career in cloud computing
Exam Format
Exam Duration
170 minutes
Number of Questions
85 questions (65 scored, 20 unscored)
Passing Score
720 out of 1000
Certification Validity
3 years
Delivery Method: Pearson VUE testing center or online proctored
Languages: English, Japanese
Topics Covered
Data Preparation for Machine Learning
28%- Feature Store
- Feature Engineering
- Feature Transformation
- Data Labeling
- Data Transformation
- Data Quality
- Data Preparation
- Data ingestion and storage
ML Model Development
26%- Model Evaluation
- Model Training
- Algorithm Selection
- Hyperparameter Tuning
- Training Optimization
- Distributed Training
- Foundation Models
- Amazon Bedrock
Deployment and Orchestration of ML Workflows
22%- SageMaker Pipelines
- Deployment Strategies
- Inference Optimization
- MLOps Best Practices
- CI/CD for ML
- Batch Transform
- Real-world Scenarios
- Model Registry
- End-to-End ML
- Advanced MLOps
- Model Deployment
- Auto Scaling
- MLOps
- Workflow Orchestration
- Multi-Model Endpoints
ML Solution Monitoring, Maintenance, and Security
24%- Security for ML
- SageMaker Model Monitor
- Cost Optimization
- Troubleshooting
- IAM for SageMaker
- VPC Configuration
- Model Monitoring
- Best Practices
The Right Way to Learn for This Exam
Theory vs Practice Balance
The MLA-C01 exam tests practical ML engineering skills. You need 30% theory (understanding ML concepts) and 70% hands-on practice (building pipelines, deploying models, monitoring).
Why Practice Tests Are Critical
ML engineering questions require understanding end-to-end workflows, service integrations, and production considerations. These decisions become intuitive through scenario-based practice.
Common Mistake to Avoid
Many candidates know ML theory but fail because they don't understand SageMaker's specific features and best practices. The exam tests practical AWS ML implementation.
How to Prepare for the Exam
Recommended Study Timeline
For Beginners
90-120 days
Dedicated study time of 1-2 hours per day
For Experienced Professionals
45-60 days
Dedicated study time of 1-2 hours per day
5-Step Preparation Strategy
Review the Official Exam Guide
Start by reading the official exam guide from Amazon Web Services (AWS) to understand what topics are covered.
Get Hands-On Experience
Practice is crucial. Set up your own test environment and work with the technologies covered in the exam.
Take Online Courses or Training
Structured courses help you understand complex concepts and fill knowledge gaps.
Practice with Realistic Exam Questions
Take practice tests to familiarize yourself with the exam format and identify weak areas. Our practice tests simulate the real exam experience.
Review and Reinforce Weak Areas
Use your practice test results to focus on topics where you need improvement before taking the real exam.
Recommended Study Resources
Preporato Practice Tests
RecommendedOur comprehensive practice test bundle includes 7 full-length practice exams with detailed explanations. Designed to simulate the real exam experience and help you identify knowledge gaps.
Official Documentation
The official Amazon Web Services (AWS) documentation is always the most authoritative source.
Visit Official Certification PageHands-On Practice
Practical experience is essential. Consider setting up a free tier account to practice with real services.
7 Mistakes That Lead to Failure (And How to Avoid Them)
Learn from the common mistakes that cause most candidates to fail. Understanding these pitfalls will help you prepare more effectively.
Insufficient SageMaker hands-on experience
Why This Is a Problem
SageMaker is central to the exam - Studio, Pipelines, Feature Store, and Model Monitor are heavily tested.
The Real Solution
Build real ML pipelines with SageMaker. Create feature stores, train models, deploy endpoints, and configure monitoring.
How Our Practice Tests Help
Our 455+ questions include 200+ SageMaker-focused scenarios covering all aspects of the platform.
Not understanding MLOps patterns
Why This Is a Problem
The exam tests production ML workflows, CI/CD, and automated retraining extensively.
The Real Solution
Practice building SageMaker Pipelines. Understand model registry, automated deployment, and A/B testing patterns.
How Our Practice Tests Help
Our practice tests include 100+ MLOps scenarios to build intuition for production patterns.
Ignoring model monitoring
Why This Is a Problem
Domain 4 (Monitoring) is 24% of the exam. Model Monitor, drift detection, and alerting are heavily tested.
The Real Solution
Understand data drift vs model drift. Know how to configure Model Monitor baselines and alerts.
How Our Practice Tests Help
Our practice tests include 80+ monitoring questions covering drift detection and maintenance.
Exam Day Tips
Before the Exam
- •Master SageMaker: Studio, Pipelines, Feature Store, Model Registry
- •Understand deployment options: real-time, batch, serverless, multi-model
- •Know Model Monitor: data drift, model quality, bias detection
- •Practice feature engineering patterns and SageMaker Processing
- •Study MLOps CI/CD patterns with CodePipeline and Step Functions
During the Exam
- •Read scenarios carefully - ML requirements drive architecture decisions
- •Identify the PRIMARY goal: training efficiency, inference latency, or cost
- •Consider MLOps maturity in each answer
- •Watch for keywords: 'production-ready', 'automated', 'scalable'
- •Eliminate answers that ignore monitoring or security
Career Benefits
Earning the AWS Certified Machine Learning Engineer - Associate certification can significantly boost your career prospects:
Certified professionals earn on average 15-20% more than non-certified peers
Many job postings require or prefer candidates with cloud certifications
Validate your skills and knowledge to employers and clients
Frequently Asked Questions
How difficult is the MLA-C01 exam?
The difficulty varies based on your experience level. With proper preparation and hands-on experience, most candidates find the exam challenging but achievable. Our practice tests help you assess your readiness.
How much does the MLA-C01 exam cost?
Exam costs vary by region and provider. Check the official Amazon Web Services (AWS) website for current pricing. Our practice tests are a cost-effective way to prepare and increase your chances of passing on the first try.
Can I retake the exam if I fail?
Yes, you can retake the exam. However, there may be waiting periods and additional fees. It's best to prepare thoroughly using practice tests to maximize your chances of passing on your first attempt.
How long should I study for the MLA-C01 exam?
Study time varies based on your background. Beginners typically need 90-120 days, while experienced professionals may need 45-60 days with 1-2 hours of daily study. Use practice tests to gauge your readiness.
How long is the certification valid?
The AWS Certified Machine Learning Engineer - Associate certification is valid for 3 years. Recertify before expiration or earn higher-level certification
Ready to Start Your Preparation?
Practice with 7 full-length exams designed to help you pass on your first try