AWS Certified Machine Learning Engineer Associate
Build a solid foundation in machine learning and data modeling with the AWS Certified Machine Learning Engineer Associate certification
About the AWS Machine Learning Engineer Associate
Explore one of the latest additions to the AWS certification portfolio
The AWS Certified Machine Learning Engineer Associate certification is designed for professionals looking to validate their expertise in building, deploying, and managing machine learning (ML) solutions on AWS.
This associate-level certification is ideal for individuals with hands-on experience in the ML lifecycle. It demonstrates a practitioner’s ability to use AWS services like SageMaker to create scalable, secure, and efficient ML workflows in the cloud.
The AWS Certified Machine Learning Engineer Associate exam assesses your skills in developing, deploying, and operating ML systems on AWS, covering the full ML lifecycle, including data preparation, model training, workflow orchestration, and monitoring:
- Data preparation
- Model development
- Model deployment
- Model monitoring and optimization
- ML workflow automation
- Troubleshooting and debugging
AWS Machine Learning Engineer Associate Exam Overview
What you need to know about the MLA-C01 Exam
Key facts
- Time: 130 minutes
- Cost: $150 USD
- Number of questions: 65
- Passing score: 720 (out of 1000), scaled scoring
- Format: Multiple-choice and multiple-response questions, ordering, matching and case studies
- Languages available: English, Japanese, Korean, Chinese
- Delivery options: Online proctored exam or in-person (Pearson VUE testing center)
Domains covered
The exam tests your knowledge across four key areas:
- Data preparation for Machine Learning (28%)
- ML model development (26%)
- Deployment and orchestration of ML workflows (22%)
- ML solution monitoring, maintenance, and security (24%)
Frequently Asked Questions
Find answers to the most frequently asked questions about the AWS Machine Learning Certification
What is the AWS Certified Machine Learning Engineer Associate Certification?
The AWS Certified Machine Learning Engineer Associate certification validates your ability to build, deploy, and manage machine learning (ML) solutions on the AWS platform.
This certification is designed for individuals with hands-on experience in the ML lifecycle, covering data engineering, model development, deployment, and operations.
Who should pursue the AWS Machine Learning Engineer Associate certification?
This certification is suitable for:
Machine Learning Engineers
Professionals responsible for building, training, and deploying machine learning models.
Data Engineers
Individuals involved in preparing, cleaning, and managing data for machine learning workflows.
AI/ML Practitioners
Those working with AWS machine learning tools like SageMaker to develop and operationalize ML solutions.
Software Developers
Developers interested in integrating ML capabilities into their applications using AWS services.
Cloud Engineers
Cloud professionals aiming to specialize in deploying scalable and secure machine learning solutions.
Is the AWS Certified Machine Learning Engineer Associate certification worth it?
The AWS Certified Machine Learning Engineer Associate certification is worth pursuing if you’re looking to advance your career in machine learning, validate your AWS expertise, or unlock opportunities in this rapidly growing field.
It’s a valuable investment for professionals aiming to specialize in cloud-based ML solutions.
- High Demand for ML Skills: Growing need for machine learning expertise across industries.
- Career Advancement: Opens doors to high-paying roles and validates your skills.
- Comprehensive Knowledge: Covers the full ML lifecycle and key AWS tools.
- Professional Recognition: Enhances credibility and positions you as a specialist.
How difficult is the AWS Certified Machine Learning Engineer Associate Exam?
The AWS Certified Machine Learning Engineer Associate exam is moderately to highly challenging, depending on your background and experience.
It tests a broad range of skills across the machine learning lifecycle, including data engineering, model development, deployment, and monitoring.
Can I take the AWS Certified Machine Learning Engineer Associate Exam from home?
Yes, AWS offers the option to take the exam online with remote proctoring, allowing you to take the exam online from the comfort of your home or office.
How long do I need to prepare for the AWS Certified Machine Learning Engineer Associate exam?
The time needed to prepare for the AWS Certified Machine Learning Engineer Associate exam depends on your experience.
- If you’re already experienced with AWS and the ML lifecycle, you might need 2-4 weeks to review key topics and take practice tests.
- Those with moderate experience in AWS and ML concepts may require 6-8 weeks to build hands-on skills and study the exam domains.
- Beginners or those new to AWS and ML should plan for 10-12 weeks or more to learn foundational concepts, gain practical experience with tools like SageMaker, and thoroughly cover the exam content.
Consistent study, hands-on practice, and mock exams are key to successful preparation.
Career Opportunities in Machine Learning & AI
Explore common roles, salaries, and essential skills to thrive in the AI and ML job market
What are the common job roles in AI and ML?
Machine Learning Engineer
- Designs, builds, and maintains ML models and algorithms.
- Often works with large datasets to train models for predictive analysis and automation.
- Tools Used: TensorFlow, PyTorch, AWS SageMaker.
Automation Specialist
- Develop automated workflows and processes using AI/ML tools on AWS.
- Optimize business operations with intelligent systems.
- Use AWS AI services to add intelligence to automation systems.
Data Scientist
- Analyzes and interprets complex data to extract insights and inform decision-making.
- Creates data models and visualizations to support AI/ML initiatives.
- Skills Required: Python, R, SQL, and data visualization tools.
AI Solutions Architect
- Designs and deploys AI/ML solutions tailored to business needs.
- Ensures scalability, security, and performance of AI systems.
- Works closely with teams to integrate AI into existing workflows.
Business Analyst
- Bridges the gap between technical AI teams and business stakeholders.
- Identifies business challenges where AI/ML can add value and measures its impact.
- Collaborate with technical teams to implement AI-driven solutions.
What Is the average salary in AI and ML?
Salaries vary based on location, industry, and expertise, with technology and finance sectors typically offering the highest pay.
Entry-Level Roles
- Start around $85,000 annually for positions like Junior Data Scientist or AI Developer.
Mid-Level Roles
- Earn between $110,000 and $135,000, such as Machine Learning Engineers with 2-5 years of experience.
Senior Roles
- Experienced professionals in roles like AI Architect or Lead Data Scientist can exceed $150,000 annually.
What skills are required for AI and ML careers?
AI and ML Concepts
- Understanding of supervised, unsupervised, and reinforcement learning.
- Familiarity with AI applications like natural language processing (NLP) and computer vision.
AWS AI Services
- Proficiency in AWS tools like SageMaker (model building), Rekognition (image analysis), and Polly (text-to-speech).
Programming Languages
- Expertise in Python or R for developing and deploying AI/ML models.
- Knowledge of frameworks like TensorFlow or PyTorch.
Data Analytics
- Ability to preprocess, analyze, and visualize data for model training.
- Familiarity with tools like Pandas, NumPy, and Matplotlib.
Problem-Solving Skills
- Critical thinking to identify challenges AI/ML can address.
- Creativity in designing solutions tailored to business goals.
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Customer Reviews
The course structure is excellent – it starts from the basics and gradually builds up to advanced concepts.
What I liked most is how practical this course is. Digital Cloud Training never disappoints. This ML Engineer Associate course prepared me thoroughly for the certification, and I passed on my first attempt!
Clear, practical, and exactly what I needed! The course broke down complex machine learning concepts into easy-to-digest lessons.
I’ve taken a few online courses before, but this one stands out. Neal explains everything so clearly, and the step-by-step demos made me feel confident enough to tackle the exam.
This is hands down the best AWS ML course I’ve taken. The instructor anticipates where students might get stuck and explains everything in simple terms. I went from feeling overwhelmed to actually enjoying the learning process.
I was nervous about starting my Machine Learning journey, but this course gave me the structure and confidence I needed.
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