The syllabus follows the Professional Machine Learning Engineer exam domains, with the most time spent on the heaviest-weighted areas and your weakest topics.
Automating and Orchestrating ML Pipelines22% of exam
Serving and Scaling Models20% of exam
Scaling Prototypes into ML Models18% of exam
Collaborating Within and Across Teams to Manage Data and Models16% of exam
Architecting Low-Code ML Solutions12% of exam
Monitoring ML Solutions12% of exam
Domain weights come from the published exam outline used in PrepKloud projects. We recheck the latest official GCP objectives with you in the first session.