AWS has introduced a new certification as of August 2024—the AWS Machine Learning Engineer – Associate certification, now available in beta. This new certification was released alongside the AWS Certified AI Practitioner exam to address the increasing demand for skilled professionals who can design, secure, and monitor machine learning and AI workloads on AWS.
Who Should Pursue the AWS Machine Learning Engineer – Associate Certification?
This certification is perfect for cloud engineers working with Amazon SageMaker who want to validate their expertise in cloud architecture, data engineering, DevOps, and machine learning. It’s also ideal for machine learning engineers who are looking to deepen their knowledge of AWS and its machine learning ecosystem.
How Does It Compare to the AWS Machine Learning – Specialty?
The Machine Learning – Specialty certification, which has been around for six years, is still highly valuable and focuses more on advanced ML algorithms, model training, and hyperparameter tuning. However, the new Machine Learning Engineer – Associate exam emphasizes managing and deploying machine learning workflows using AWS services like SageMaker, making it more accessible for those seeking practical, hands-on experience with AWS’s machine learning offerings.
Structure of the MLA-C01 Exam
The exam includes 85 questions that must be completed in 170 minutes. It features multiple choice and multiple select questions, as well as new question types like ordering, matching, and case studies. If you plan to take this exam, it’s a good idea to familiarize yourself with these new formats.
You can choose to take the exam at a testing center or via online proctoring from the comfort of your home, depending on your preference.
What the AWS Machine Learning Engineer – Associate Exam Covers
The exam is divided into four domains:
- Data Preparation for Machine Learning
- ML Model Development
- Deployment and Orchestration of ML Workflows
- ML Solution Monitoring, Maintenance, and Security
A significant portion of the exam revolves around Amazon SageMaker, AWS’s integrated service for building, training, and deploying machine learning models. Expect to see questions covering every stage of the machine learning lifecycle, from data ingestion to model retraining and security.
Key SageMaker components to study include:
- SageMaker Data Wrangler for data preparation and feature engineering
- SageMaker Model Registry for organizing and managing machine learning models
- SageMaker Inference for deploying models using real-time, serverless, or batch endpoints
General AI/ML Concepts to Know
Beyond SageMaker, you’ll need a solid understanding of fundamental machine learning concepts such as:
- Regression vs. classification models
- Metrics for model tuning and monitoring
- Overfitting and underfitting problems and solutions
You’ll also need to be familiar with standalone AWS AI/ML services like Amazon Bedrock for Generative AI, and Amazon Comprehend for sentiment analysis and data redaction.
Ready to Take the Exam?
While AWS certifications offer valuable recognition of your knowledge, hands-on experience will be essential to passing this new exam. If you’re looking to enhance your preparation, consider using AWS dumps to test your knowledge and gain confidence before taking the exam.
Visit DumpsForAWS.com to access AWS dumps for the Machine Learning Engineer – Associate exam and other AWS certifications. Our resources will help you practice and solidify your understanding, giving you the best chance to succeed on exam day.
Good luck on your certification journey!
