AWS Certified Machine Learning Engineer - Associate Exam Overview

The exam covers four official domains: Data Preparation for Machine Learning, ML Model Development, Deployment and Orchestration of ML Workflows, and ML Solution Monitoring, Maintenance, and Security. AWS publishes the weights as 28%, 26%, 22%, and 24% of scored content respectively. Use those official domains rather than local practice-bank categories.

AWS describes MLA-C01 as a 130-minute exam with 65 total questions, including 50 scored and 15 unscored questions. The guide lists multiple-choice, multiple-response, ordering, and matching question types. The important preparation takeaway is that scenarios can test sequence and service fit, not just recognition of a single SageMaker feature.

The target candidate has experience using Amazon SageMaker and other AWS services for ML engineering, plus related experience such as backend development, DevOps, data engineering, or data science. The guide also states that full end-to-end ML solution architecture and deep work across multiple ML domains are outside the expected target-candidate tasks.

Prioritize production ML tasks: ingesting and validating data, transforming features, training and tuning models, evaluating model performance, managing model versions, choosing deployment infrastructure, configuring endpoints and auto scaling, orchestrating workflows with CI/CD, monitoring data and model quality, and securing ML resources with IAM, encryption, and network controls.