How I Passed the AWS Certified Machine Learning Engineer - Associate Exam (MLA-C01)
Introduction
I recently earned the AWS Certified Machine Learning Engineer - Associate certification, and I’m excited to share my journey. This certification validates expertise in building, deploying, and managing machine learning (ML) solutions on AWS, covering the entire ML lifecycle from data preparation to monitoring and maintaining deployed models.
In this blog, I’ll walk you through the exam prerequisites, content outline, preparation strategies, and resources I used to pass the MLA-C01 exam.
Exam Prerequisites
Before attempting this certification, AWS recommends the following experience and knowledge:
Experience:
At least 1 year of experience using Amazon SageMaker and other AWS ML services.
Related roles like backend software developer, DevOps developer, data engineer, or data scientist are helpful.
Knowledge:
General IT Knowledge:
Understanding ML algorithms and use cases.
Data engineering fundamentals, including ingestion, transformation, and querying data.
Familiarity with CI/CD pipelines, modular code development, and debugging.
AWS-Specific Knowledge:
Deep familiarity with Amazon SageMaker capabilities, algorithms, and deployment.
Understanding AWS services for storage, processing, automation, and CI/CD.
Knowledge of AWS security best practices, such as identity and access management (IAM) and encryption.
Exam Overview
Feature | Details |
Level | Associate |
Length | 180 minutes |
Cost | $150 USD |
Format | 65 questions (MCQs/multiple response) |
Delivery | Pearson VUE or PSI (online or in testing centers) |
Content Outline
The exam covers the following domains, with weightings indicating their importance:
Domain | Weight |
Data Preparation for Machine Learning (ML) | 28% |
ML Model Development | 26% |
Deployment and Orchestration of ML Workflows | 22% |
ML Solution Monitoring, Maintenance, and Security | 24% |
Total | 100% |
How I Prepared
📚 Courses I Took
AWS Machine Learning Engineer Learning Path:
Comprehensive official training covering data preparation, SageMaker, and CI/CD workflows.AWS Certified Machine Learning Engineer Associate: Hands On! by Stephane Maarek and Frank Kane:
Excellent for building a solid understanding of AWS ML services, algorithms, and use cases.
🛠️ Hands-On Projects
Practical experience is critical for mastering ML concepts and AWS services. Here’s what I worked on:
Data Preparation:
Ingesting and transforming data using AWS Glue, Amazon Athena, and Amazon S3.Model Training and Tuning:
Training models with Amazon SageMaker, performing hyperparameter tuning, and managing model versions.Deployment Pipelines:
Setting up CI/CD pipelines using AWS CodePipeline and deploying models via SageMaker endpoints.Monitoring and Maintenance:
Using Amazon CloudWatch, AWS Lambda, and SageMaker Model Monitor to detect anomalies and automate responses.
👉 Check out my GitHub repository for detailed code and projects.
📋 Study Resources
AWS Documentation:
Tutorials Dojo Practice Exams:
High-quality practice exams with detailed explanations for each question.AWS Ramp-Up Guide for Machine Learning:
Structured resource path for ML concepts and AWS services.Study Groups and Communities:
Tech Study Slack: Engaged with peers preparing for similar certifications.
Cloud and DevOps Babies: Global community sharing resources and study tips.
Study Tips and Tricks
Allocate Time Effectively: Create a study schedule to cover all exam domains, dedicating more time to higher-weighted areas.
Hands-On Practice: Use AWS Free Tier or personal projects to solidify concepts.
Flagging Questions: Use the exam's flagging option for uncertain questions and revisit them later.
Attempt All Questions: There’s no penalty for incorrect answers, so ensure you answer every question.
Conclusion
Passing the AWS Certified Machine Learning Engineer - Associate exam requires dedication, practical experience, and a well-rounded understanding of AWS ML services. By following a structured study plan and leveraging the resources mentioned, I was able to succeed—and you can too!
Let me know if you have any questions or need help with your preparation. Connect with me for more insights and resources on LinkedIn, Twitter, and GitHub.