CERTALYTH Certified AI/ML Engineer – Associate

CCAIML-A · CT-AIML-A · Machine Learning and AI Engineering on AWS

Build, train, and operationalize ML models on AWS.

AWS Certified Machine Learning Engineer – Associate (MLA-C01)

40 hoursCertificate with unique codeMultiple-choice exam

199EUR

Full bundle: course PDF + online exam + certification.

Recertification every 3 years: 99 EUR

Instant access after payment
Schedule your online exam
Publicly verifiable certificate

About the course

Learn to design machine learning solutions on AWS, from data preparation and transformation to training, evaluation, deployment, and production monitoring. Aligned with the AWS Certified Machine Learning Engineer – Associate exam.

Focus: End-to-end ML engineering on AWS: data preparation, training, deployment, and MLOps.

What you'll learn

Prepare and transform data for ML on AWS

Train and evaluate models with AWS services

Implement MLOps pipelines and deployment

Monitor and optimize models in production

Sample questions (free)

A sample from the official exam set — correct answers are revealed only during the actual exam.

CERTALYTH Certified AI/ML Engineer – Associate

About the course

What you'll learn

Sample questions (free)

You need to store multiple terabytes of raw training data for reproducible experiments in SageMaker. Which storage option provides the best combination of cost-effectiveness, native SageMaker integration, and lifecycle controls for archival?

A streaming clickstream pipeline must produce hourly parquet files in S3 for downstream training. Which architecture minimizes end-to-end latency while ensuring at-least-once delivery and compatibility with Spark-based preprocessing?

You must enforce column-level encryption for sensitive attributes in a dataset while retaining the ability to run feature transformations and joins. Which approach balances security and usability for training jobs?

Which SageMaker feature is best suited for storing, serving, and governing engineered features across training and inference to ensure consistency?

You need a reliable metric to evaluate a heavily imbalanced binary classification problem where false positives are much less costly than false negatives. Which metric should you prioritize when tuning models?