Study guides for Professional Machine Learning Engineer

Professional Machine Learning Engineer Study Guides

The Google Cloud Professional Machine Learning Engineer certification validates that you can build, evaluate, productionize, and optimize AI solutions using Google Cloud capabilities. The exam covers six Domains: architecting low-code AI solutions; collaborating within and across teams to manage data and models; scaling prototypes into ML models; serving and scaling models; automating and orchestrating ML pipelines; and monitoring AI solutions. Expect scenarios built around Gemini Enterprise Agent Platform, BigQuery ML, and responsible AI practices.

Domains

Architecting low-code AI solutions

Developing ML models using BigQuery ML or AutoML on Gemini Enterprise Agent Platform is low-code training where SQL, automated search, and managed pipelines replace custom code. BigQuery ML trains mod…

Study Guide Practice Test

Collaborating within and across teams to manage data and models

Organizing and preprocessing data for machine learning means placing each data type where distributed training can read it at high speed. BigQuery holds tabular data as materialized tables, while Clou…

Study Guide Practice Test

Scaling prototypes into ML models

Building models given the task means matching data shape, problem type, and operational limits to the model family, training product, deployment path, and explanation method. Ordered numeric series po…

Study Guide Practice Test

Serving and scaling models

Batch and online inference are two distinct serving patterns for different latency and throughput goals. Vertex AI Batch Prediction is an asynchronous service where data flows from a Cloud Storage…

Study Guide Practice Test

Automating and orchestrating ML pipelines

Developing end-to-end ML pipelines is the practice of connecting data validation, preprocessing, training, evaluation, and deployment into one automated and traceable system. Gemini Enterprise Agent P…

Study Guide Practice Test

Monitoring AI solutions

Identifying risks to AI solutions is the layered defense that keeps model weights, training data, prompts, and outputs inside authorized boundaries and prevents attacker text from steering behavior. T…

Study Guide Practice Test

Professional Machine Learning Engineer Study Guides

Domains

Architecting low-code AI solutions

Study Guide Practice Test

Collaborating within and across teams to manage data and models

Study Guide Practice Test

Scaling prototypes into ML models

Study Guide Practice Test

Serving and scaling models

Study Guide Practice Test

Automating and orchestrating ML pipelines

Study Guide Practice Test

Monitoring AI solutions

Study Guide Practice Test