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Who should take the Google Professional Data Engineer exam

Individuals should pursue the exam if they want to demonstrate their expertise and ability to design and develop Data Engineering. Following professional get benefited from Google Professional Data Engineer Certification

• Business analysts
• Data analysts
• Data engineers
• Data architects
• Developers responsible for managing big data transformation initiatives
• Data scientists

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Google Professional-Data-Engineer日本語 Exam Syllabus Topics:

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

Requirements

The certification does not have any official prerequisites. However, it is advised to have at least three years of industry experience with one or more years of expertise in designing and managing different solutions with the use of Google Cloud Platform. It is also required to review the topics of the qualifying exam before sitting for it.

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Operationalize ML Models

• 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;
• 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);
• 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;
• 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.