AWS Machine Learning Engineer Associate Exam Prep

The ML Engineer Associate exam covers the complete ML lifecycle: data preparation, model training, hyperparameter tuning, deployment, and monitoring. Expect questions on SageMaker (training jobs, endpoints, processing jobs, pipelines, feature store, model registry), data transformation with AWS Glue, and feature engineering best practices.

A significant portion of the exam focuses on ML operations (22%) — deploying models to production endpoints, setting up A/B testing with traffic splitting, configuring auto-scaling for inference endpoints, and monitoring model performance for drift. The security domain (24%) covers data encryption, VPC configurations for SageMaker, IAM roles, and ML governance with SageMaker Model Cards and Model Dashboard.

This exam requires both ML knowledge and AWS service expertise. Start by understanding the SageMaker ecosystem thoroughly — it's the core of most exam questions. Know the differences between SageMaker built-in algorithms (XGBoost, Linear Learner, Image Classification), how to bring your own container, and when to use SageMaker vs managed AI services like Comprehend or Rekognition.

For the data engineering domain (28%), understand data pipelines using Glue, Kinesis, and S3, and how to handle common data quality issues. Practice with questions about choosing the right instance type for training vs inference, and know how to optimize costs with Spot instances for training jobs.

The ML Engineer Associate fills a critical gap in the AWS certification portfolio — it validates practical ML engineering skills that are in extreme demand. As companies move from ML experimentation to production deployment, the need for engineers who can build reliable ML pipelines on AWS is growing rapidly. Salaries for certified ML engineers typically range from $140,000 to $180,000.

Recommended prerequisites include 1-2 years of experience with ML/data science concepts and familiarity with AWS services. The AI Practitioner certification provides useful foundational knowledge. This is also a natural stepping stone toward the Machine Learning Specialty for those wanting deeper expertise.