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About the Certification

This is an Associate-level AWS certification designed for professionals who work in Machine Learning Engineering or in roles closely related to building and operating AI and machine learning solutions on AWS. It focuses on building, operating, deploying, and maintaining ML solutions and pipelines using AWS services in a practical way.

It also validates your ability to prepare data, develop models, organize workflows, and monitor and secure ML solutions within AWS environments.

Main Curriculum

The curriculum is divided into four main sections

  • Monitoring, Maintaining, and Securing ML Solutions
    • Monitoring models and pipelines
    • Maintaining solutions after deployment
    • Detecting issues and improving performance
    • Security for machine learning solutions within AWS
  • Developing Machine Learning Models
    • Selecting appropriate modeling approaches
    • Training models
    • Hyperparameter tuning
    • Analyzing model performance
    • Managing model versions
  • Deploying and Orchestrating ML Workflows
    • Model deployment
    • Orchestration of ML workflows
    • Building and running pipelines
    • Operating solutions in production environments on AWS
  • Preparing Data for Machine Learning
    • Collecting and preparing data
    • Cleaning and transforming data
    • Validating data quality
    • Preparing data to be suitable for training processes

Exam Details :

  • Exam code: MLA-C01
  • Exam duration: 130 minutes
  • Number of questions: 65 questions
  • Question types: Multiple choice and multiple response
  • Language: English
  • Passing score: 720 out of 1000
  • Exam price: $150, and taxes may be added in some countries depending on local regulations
  • Certification validity: 3 years

Do I need to earn any certifications before this one ?

No, it is not required to hold an AWS certification before taking this one.

However, AWS explains that the ideal candidate for this certification usually has one year or more of experience in Machine Learning Engineering or a related field, in addition to one year or more of hands-on experience using AWS services. AWS also notes that candidates without prior ML experience can begin with official preparation paths and build their knowledge and skills gradually.

Which certifications or paths are recommended after this one ?

It is not mandatory.
However, if you want to grow further after AWS Certified Machine Learning Engineer - Associate, suitable next directions often include:
AWS Certified Machine Learning – Specialty to go deeper into designing, building, and deploying advanced ML solutions on AWS, which AWS directly recommends as a next step for those who want to dive further into machine learning
Data Engineering track to strengthen the data side, pipelines, and large-scale data preparation
MLOps track to expand into model management, automation, deployment, CI/CD, and monitoring in production environments
MLOps track to expand into model management, automation, deployment, CI/CD, and monitoring in production environments
Generative AI track to specialize further in LLMs, Foundation Models, and modern AI applications on AWS
AWS Solutions Architect Associate to connect ML solutions with cloud design and the right supporting infrastructure
Security / Governance for AI to strengthen data protection, compliance, and the security of AI solutions

What are the main uses of AWS Certified Machine Learning Engineer - Associate ?

  • Machine Learning Engineer roles for building, operating, and deploying ML solutions on AWS in a practical way
  • MLOps track to deepen expertise in pipelines, orchestration, and model monitoring after deployment in production environments
  • Data Engineering track to strengthen data preparation, transformation, and validation before building and running models
  • Model Deployment and Monitoring to specialize in deploying models, maintaining them, and continuously improving their performance on AWS
  • AWS Certified Machine Learning – Specialty as a next step for advancing further in the AWS ML path, which AWS presents as part of the AI/ML journey
  • Generative AI track to expand later into modern AI applications and Foundation Models within the AWS ecosystem
  • AWS Solutions Architect Associate to connect ML solutions with suitable cloud architecture and infrastructure on AWS
  • Applied AI / ML Implementation roles to strengthen your opportunities in practical jobs related to implementing AI and machine learning solutions on AWS

Your strong step into practical Machine Learning on AWS :

The AWS Certified Machine Learning Engineer – Associate certification is a strong step for anyone who wants to prove their ability to build, deploy, operate, and monitor machine learning solutions on AWS in a practical, production-focused way.

Who is this certification suitable for ?

  • You work in Machine Learning Engineering
  • You want to enter the MLOps field
  • You already have AWS experience and want to apply it to ML projects
  • You want a certification that proves your ability to build, deploy, and monitor ML solutions
  • You are targeting practical AI roles in production environments
  • You want a strong professional step before moving later into Machine Learning – Specialty or other advanced AWS paths

In short :

It is one of AWS’s most important modern certifications for anyone who wants to prove themselves in implementing and operating Machine Learning solutions on AWS in a practical way, because it focuses on the full workflow: from data, to model, to deployment, to monitoring and maintenance in production environments.

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