Google Professional-Data-Engineer日本語 : Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)

Professional-Data-Engineer日本語 real exams

Exam Code: Professional-Data-Engineer-JPN

Exam Name: Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)

Updated: Jul 12, 2026

Q & A: 433 Questions and Answers

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Exam Details

The Google Professional Data Engineer certification exam has the duration of 2 hours. The qualifying test is made up of multiple-select and multiple-choice questions. The exam is available either in Japanese or English. To register for it, you are required to go through the official webpage and pay the fee of $200 plus applicable taxes. While completing the registration process, the potential individuals can choose the preferred method of exam delivery. It can be taken in person at the nearest testing center or online from a remote location.

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

TopicDetails
Topic 1
  • Designing data processing systems: It delves into designing for security and compliance, reliability and fidelity, flexibility and portability, and data migrations.
Topic 2
  • Ingesting and processing the data: The topic discusses planning of the data pipelines, building the pipelines, acquisition and import of data, and deploying and operationalizing the pipelines.
Topic 3
  • Maintaining and automating data workloads: It discusses optimizing resources, automation and repeatability design, and organization of workloads as per business requirements. Lastly, the topic explains monitoring and troubleshooting processes and maintaining awareness of failures.
Topic 4
  • Storing the data: This topic explains how to select storage systems and how to plan using a data warehouse. Additionally, it discusses how to design for a data mesh.
Topic 5
  • Preparing and using data for analysis: Questions about data for visualization, data sharing, and assessment of data may appear.

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

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

The following will be discussed here:

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

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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