The new Professional Machine Learning Engineer exam will be live starting November 20. If you plan to take the Machine Learning Engineer exam on or after November 20, please review the new GA exam guide. The new exam does not cover generative AI, as the tools used to develop generative AI-based solutions are evolving quickly. If you are interested in generative AI, please refer to the Introduction to Generative AI Learning Path (all audiences) or the Generative AI for Developers Learning Path (technical audience). If you are a partner, please refer to the Gen AI partner courses: Introduction to Generative AI Learning PathGenerative AI for ML Engineers, and Generative AI for Developers.

Certification Exam Guide

A Professional Machine Learning Engineer builds, evaluates, productionizes, and optimizes ML models by using Google Cloud technologies and knowledge of proven models and techniques. The ML Engineer handles large, complex datasets and creates repeatable, reusable code. The ML Engineer considers responsible AI and fairness throughout the ML model development process, and collaborates closely with other job roles to ensure long-term success of ML-based applications. The ML Engineer has strong programming skills and experience with data platforms and distributed data processing tools. The ML Engineer is proficient in the areas of model architecture, data and ML pipeline creation, and metrics interpretation. The ML Engineer is familiar with foundational concepts of MLOps, application development, infrastructure management, data engineering, and data governance. The ML Engineer makes ML accessible and enables teams across the organization. By training, retraining, deploying, scheduling, monitoring, and improving models, the ML Engineer designs and creates scalable,
performant solutions.

*Note: The exam does not directly assess coding skill. If you have a minimum proficiency in Python and Cloud SQL, you should be able to interpret any questions with code snippets.

Section 1: Architecting low-code ML solutions

1.1 Developing ML models by using BigQuery ML. Considerations include:

  1. Building the appropriate BigQuery ML model (e.g., linear and binary classification, regression, time-series, matrix factorization, boosted trees, autoencoders) based on the business problem.
  2. Feature engineering or selection by using BigQuery ML.
  3. Generating predictions by using BigQuery ML.

1.2 Building AI solutions by using ML APIs. Considerations include: 1.2 Building AI solutions by using ML APIs. Considerations include:

  1. Building applications by using ML APIs (e.g., Cloud Vision API, Natural Language API, Cloud Speech API, Translation).
  2. Building applications by using industry-specific APIs (e.g., Document AI API, Retail API).

1.3 Training models by using AutoML. Considerations include:

  1. Preparing data for AutoML (e.g., feature selection, data labeling, Tabular Workflows on AutoML)
  2. Using available data (e.g., tabular, text, speech, images, videos) to train custom models
  3. Using AutoML for tabular data
  4. Creating forecasting models using AutoML
  5. Configuring and debugging trained models

Section 2: Collaborating within and across teams to manage data and models

For more detail please visit the official link

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