How I Passed the AWS Certified Machine Learning Engineer - Associate Exam (MLA-C01)

How I Passed the AWS Certified Machine Learning Engineer - Associate Exam (MLA-C01)

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4 min read

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

FeatureDetails
LevelAssociate
Length180 minutes
Cost$150 USD
Format65 questions (MCQs/multiple response)
DeliveryPearson VUE or PSI (online or in testing centers)

Content Outline

The exam covers the following domains, with weightings indicating their importance:

DomainWeight
Data Preparation for Machine Learning (ML)28%
ML Model Development26%
Deployment and Orchestration of ML Workflows22%
ML Solution Monitoring, Maintenance, and Security24%
Total100%

How I Prepared

📚 Courses I Took

  1. AWS Machine Learning Engineer Learning Path:
    Comprehensive official training covering data preparation, SageMaker, and
    CI/CD workflows.

  2. 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

  1. AWS Documentation:

    • Amazon SageMaker Developer Guide

    • AWS CI/CD for Machine Learning

  2. Tutorials Dojo Practice Exams:
    High-quality practice exams with detailed explanations for each question.

  3. AWS Ramp-Up Guide for Machine Learning:
    Structured resource path for ML concepts and AWS services.

  4. 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

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.

Good luck! 🚀
👉 LinkedIn
👉 Twitter
👉 GitHub

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