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

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

Exam Topics

The syllabus of the Google Professional Data Engineer exam is divided into 4 topics, each covering specific knowledge and skills that the candidates need to develop while preparing for the test. A full outline of the exam content can be viewed on the official website. The highlights of the domains covered in the test are as follows:

Topic 1. Designing Data Processing Systems

To answer the questions related to this first topic of the certification exam, the individuals need to demonstrate their proficiency in selecting the proper storage technologies. This includes their understanding of data modeling, schema design, distributed systems, as well as tradeoffs involving throughput, latency, and transactions. Moreover, the applicants need to have the ability to map storage systems to the business needs. It also measures one’s skills in designing data pipelines, designing a data processing solution, as well as migrating data warehousing & data processing.

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Understanding functional and technical aspects of Google Professional Data Engineer Exam Operationalizing machine learning models

The following will be discussed here:

• Common sources of error (e.g., assumptions about data)
• Use of edge compute
• Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
• Measuring, monitoring, and troubleshooting machine learning models
• Choosing the appropriate training and serving infrastructure
• Continuous evaluation
• Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
• Deploying an ML pipeline
• Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
• Operationalizing machine learning models
• Impact of dependencies of machine learning models
• ML APIs (e.g., Vision API, Speech API)
• Distributed vs. single machine
• Hardware accelerators (e.g., GPU, TPU)
• Ingesting appropriate data
• Conversational experiences (e.g., Dialogflow)
• Leveraging pre-built ML models as a service