Exam Overview

The Professional Data Engineer certification exam is a 2-hour test consisting of the multiple-choice and multiple-select questions. The students can take it in the English or Japanese languages. To register for and schedule the exam, you must pay the fee of $200. It is possible to sit for this test in an online proctored format at a remote location. You can also take it as an on-site proctored exam at a designated testing center.

The Professional Data Engineer exam is the industry-standard exam that proves the candidate’s ability to do data-driven decision-making by assembling, transforming, and publishing data. If you are rooting for a career in data engineering, you should take this test. It will lead you to attain the Professional Data Engineer certification issued by Google.

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Google Professional-Data-Engineer Exam Syllabus Topics:

Reference: https://cloud.google.com/certification/data-engineer

Operationalize ML Models

• Measure, Troubleshoot & Monitor Machine Learning Models: The focus of this subtopic includes the effect of dependencies on machine learning models. It will also measure the examinees’ understanding of machine learning terminologies, such as features, regression, labels, classification, models, recommendation, evaluation metrics, and unsupervised & supervised learning. Moreover, it will also assess their knowledge of common sources of error such as assumptions regarding data.
• Leverage Pre-Built Machine Learning Models as a Service: It covers one’s knowledge and skills in customizing machine learning APIs, including Auto ML text and Auto ML Vision. It also covers the conversational experiences, such as Dialogflow as well as machine learning APIs, including Speech API and Vision API;
• Select the Relevant Training & Service Infrastructure: The consideration for this topic includes distributed versus single machine, hardware accelerators (such as TPU and GPU), and edge compute usage;
• Deploy Machine Learning Pipelines: This objective requires your competence in ingesting relevant data, continuous evaluation, and retraining of ML models (Kuberflow, BigQuery Machine Learning, Cloud Machine Learning Engine, and Spark Machine Learning);