AWS Certified Machine Learning Associate (MLA-C01) Practice Exams 2026

About the AWS MLA-C01 Exam

The AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam validates your ability to build, deploy, and maintain production-grade machine learning solutions on AWS. Launched in 2024, this certification specifically targets ML engineering roles focused on implementing and operationalizing ML pipelines using AWS services like SageMaker, Lambda, Step Functions, and Glue.

The exam consists of 65 questions (multiple-choice and multiple-response) that you need to complete in 170 minutes. AWS uses a scaled scoring model from 100-1000, with a passing score of 720. The exam costs $150 USD and is available at Pearson VUE testing centers or via online proctoring. Your certification remains valid for three years from the date you pass.

Exam Domains and Weighting:

• Domain 1: Data Preparation for Machine Learning (30%) - Data ingestion with Glue and Kinesis, feature engineering, data transformation pipelines, data validation, and preprocessing workflows using SageMaker Processing and EMR.
• Domain 2: Model Development (25%) - Training models with SageMaker Training Jobs, hyperparameter tuning with Automatic Model Tuning, distributed training strategies, custom algorithms with Docker containers, and SageMaker Autopilot for AutoML.
• Domain 3: Deployment and Orchestration (25%) - SageMaker endpoints (real-time and batch), model deployment strategies (A/B testing, canary deployments), Step Functions for ML workflow orchestration, Lambda for inference, and SageMaker Pipelines for MLOps.
• Domain 4: ML Solution Monitoring, Maintenance, and Security (20%) - CloudWatch for model monitoring, SageMaker Model Monitor for data drift detection, EventBridge for event-driven ML workflows, IAM for access control, KMS for encryption, and SageMaker Model Registry for model versioning.

This exam is designed for ML engineers with 1-2 years of hands-on experience building and deploying ML solutions on AWS. Strong understanding of Python, ML frameworks (TensorFlow, PyTorch, Scikit-learn), and AWS services is essential. New to AWS ML? Start with the AWS Cloud Practitioner (CLF-C02) to learn cloud fundamentals, then progress to MLA-C01.

Why Take This Certification?

• High-Demand ML Engineering Role: AWS ML Engineers with MLA-C01 certification earn an average of $130,000 annually in the United States (Source: Global Knowledge IT Skills Report 2024-2025), with experienced professionals commanding $140,000-$165,000. The certification validates production-grade ML engineering skills that companies desperately need.
• Newest AWS Certification Path: Launched in 2024, MLA-C01 represents AWS's commitment to the growing MLOps discipline. Early adopters gain a competitive advantage as this certification becomes the industry standard for ML engineering roles, positioning you ahead of peers still holding older ML certifications.
• Production-Focused ML Skills: Unlike the older Machine Learning Specialty (MLS-C01), the MLA-C01 focuses on engineering and operationalizing ML pipelines - building SageMaker Pipelines, orchestrating with Step Functions, monitoring with CloudWatch, and implementing MLOps best practices. These are the skills companies need to move ML models from notebooks to production.
• Gateway to Advanced ML Roles: The MLA-C01 serves as the foundation for ML architect and senior ML engineer positions. Companies like Amazon, Netflix, and Airbnb specifically seek candidates with AWS ML certifications for their ML platform teams, making this certification a direct pathway to elite tech companies.

What You'll Learn in the MLA-C01 Exam

The AWS MLA-C01 exam covers the complete ML engineering lifecycle on AWS, from data preparation through model monitoring in production. You'll demonstrate proficiency across the entire ML pipeline, focusing on engineering best practices and operationalizing ML workflows.

Core AWS ML Services

• SageMaker Ecosystem: Training Jobs, Processing Jobs, Pipelines, Model Registry, Endpoints (real-time and batch), Automatic Model Tuning, Autopilot, Feature Store, Clarify for bias detection, and Model Monitor for drift detection.
• Data Engineering: AWS Glue for ETL, Glue Data Catalog, Athena for SQL queries, EMR for large-scale processing, Kinesis for real-time data streams, and Data Pipeline for workflow orchestration.
• ML Orchestration: Step Functions for ML workflow coordination, Lambda for serverless inference and preprocessing, EventBridge for event-driven ML automation, and SageMaker Pipelines for CI/CD.
• Monitoring and Security: CloudWatch for metrics and logs, CloudTrail for auditing, IAM for access control, KMS for encryption at rest, and VPC for network isolation.

ML Engineering Concepts

• Designing scalable data preprocessing pipelines using SageMaker Processing and Spark on EMR
• Implementing distributed training with SageMaker managed spot training and Pipe mode for S3 streaming
• Building A/B testing frameworks with SageMaker endpoint variants for production model evaluation
• Detecting data drift and model performance degradation with SageMaker Model Monitor
• Orchestrating end-to-end ML workflows with Step Functions and SageMaker Pipelines
• Implementing MLOps practices including model versioning, lineage tracking, and automated retraining

How to Prepare for the MLA-C01 Exam

1. Master SageMaker Services (4-5 weeks): Work through the official AWS MLA-C01 exam guide and focus heavily on SageMaker components - Training Jobs, Processing, Pipelines, and Model Monitor. Build at least 3-4 complete ML pipelines using SageMaker Pipelines to understand orchestration. Deploy models using both real-time endpoints and batch transform jobs.
2. Practice ML Engineering on AWS (3-4 weeks): Create hands-on projects that implement the full ML lifecycle: data ingestion with Glue, feature engineering with SageMaker Processing, training with hyperparameter tuning, deployment with A/B testing, and monitoring with Model Monitor. Use the AWS Free Tier and SageMaker Studio notebooks for practice. Build at least one end-to-end project from raw data to production deployment.
3. Study Data Engineering for ML (2 weeks): Learn AWS Glue, Athena, and EMR for data preparation at scale. Understand when to use Kinesis vs. Glue for different data ingestion patterns. Practice writing Spark jobs for feature engineering. This domain accounts for 30% of the exam, making it critical to master.
4. Practice Exams and Review (1-2 weeks): Take timed practice exams to identify weak areas. Review AWS whitepapers on MLOps best practices and the SageMaker Developer Guide. Focus on understanding trade-offs between different approaches (e.g., real-time vs. batch inference, managed vs. custom algorithms).

Download the official AWS MLA-C01 exam guide and review the SageMaker Developer Guide before starting your preparation. The exam is highly practical, so hands-on experience with SageMaker is essential for passing.

Frequently Asked Questions