It is aimed at those who need to verify AWS end-to-end ML engineering capabilities, including data engineers, AI algorithm developers, and cloud computing architects. It is especially suitable for practitioners who plan to transform into MLOps or need AWS official endorsement to improve their job competitiveness.

65 questions/180 minutes, including single-choice, multiple-choice, and situational analysis questions (weight 50%). The passing score is 750/1000 points. The experimental questions focus on SageMaker Pipeline construction and XGBoost hyperparameter tuning.

The core assessment includes SageMaker (feature engineering/model registration/Endpoints), Lambda function deployment, EC2 instance optimization, CloudFormation automation, and S3 and Glue integration in data lake scenarios.

At least 6 months of AWS ML practical experience is required. It is recommended to obtain the Cloud Practitioner Basic Certification first, master the basics of Python+SQL, and focus on the "SageMaker Full Stack Development Guide" to shorten the learning curve.