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Updated: Jul 29, 2026
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Google Professional-Data-Engineer日本語 Exam Overview:
| Certification Vendor: | Google Cloud |
| Exam Name: | Google Cloud Certified Professional Data Engineer Exam |
| Exam Number: | Professional Data Engineer |
| Passing Score: | Not publicly disclosed (scaled scoring system) |
| Certificate Validity Period: | 2 years |
| Exam Price: | $200 USD |
| Exam Duration: | 120 minutes |
| Real Exam Qty: | Approximately 50–60 multiple choice and multiple select questions |
| Exam Format: | multiple choice, multiple select, case study |
| Related Certifications: | Google Cloud Certified - Associate Cloud Engineer Google Cloud Certified - Professional Cloud Architect Google Cloud Certified - Professional Machine Learning Engineer Google Cloud Certified - Professional Data Analytics Engineer |
| Available Languages: | English, Japanese, Spanish, Portuguese, French, German |
| Recommended Training: | Google Cloud Professional Data Engineer Course (Coursera) Google Cloud Data Engineering Learning Path |
| Exam Registration: | Google Cloud Certification Registration |
| Sample Questions: | Google Professional-Data-Engineer日本語 Sample Questions |
| Exam Way: | Online proctored exam or onsite test center (Kryterion Webassessor) |
| Pre Condition: | No formal prerequisites required; recommended experience with data processing and Google Cloud Platform services |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/data-engineer |
Google Cloud Big Data & Machine Learning Fundamentals course
This course is a gateway to introduce you to Google Cloud's big data and different machine learning functions. However, to successfully pass this training, you have to attain one year of experience in SQL, extract transform, data modeling, machine learning, programming in Python, and load activities. So, the objectives of the course are the following:
- Recognize the purpose of the key Big data and Machine Learning products in Google Cloud
- Hire BigQuery and Cloud SQL for interactive data analysis
- Utilize Cloud SQL & Dataproc to migrate existing MySQL, Pig, Spark, or Hive workloads to Google Cloud
- Create ML models using BigQuery ML, APIs, and AutoML.
Operationalize ML Models
- 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.
- 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;
Reference: https://cloud.google.com/certification/data-engineer
Google Professional-Data-Engineer日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Building and operationalizing data processing systems | 24% | - Data processing and transformation
|
| Topic 2: Operationalizing machine learning models | 26% | - Model deployment and monitoring
|
| Topic 3: Designing data processing systems | 22% | - Batch and streaming data processing design
|
| Topic 4: Ensuring solution quality | 28% | - Reliability and performance
|
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