Try Google : Professional-Data-Engineer日本語 valid & accurate questions and answers

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 Professional Data Engineer Exam
Exam Number:Professional-Data-Engineer
Real Exam Qty:40 - 50
Exam Price:USD 200 (plus tax where applicable)
Passing Score:700 / 1000
Exam Duration:120 minutes
Certificate Validity Period:2 years
Available Languages:English, Japanese
Related Certifications:Google Cloud Associate Cloud Engineer
Google Cloud Professional Cloud Architect
Google Cloud Professional Data Analyst
Exam Format:Multiple choice, Multiple select
Recommended Training:Official Exam Guide
Google Cloud Professional Data Engineer Learning Path
Exam Registration:Google Cloud Certification Registration
Sample Questions:Google Professional-Data-Engineer日本語 Sample Questions
Exam Way:Online-proctored or onsite-proctored
Pre Condition:No mandatory prerequisites; recommended 3+ years industry experience, including 1+ year designing and managing Google Cloud data solutions
Official Syllabus URL:https://cloud.google.com/learn/certification/data-engineer

Building & Operationalizing Data Processing Systems

Within this subject area, the test takers should show that they know how to build and operationalize storage systems. Specifically, they need to be conversant with effective use of managed services (such as Cloud Bigtable, Cloud SQL, Cloud Spanner, BigQuery, Cloud Storage, Cloud Memorystore, Cloud Datastore), storage costs & performance, and lifecycle management of data. The students should also be capable of building as well as operationalizing pipelines, including such technical tasks as data cleansing, transformation, batch & streaming, data acquisition & import, and integrating with new data sources. Apart from that, the candidates need to have sufficient competency to build and operationalize the processing infrastructure. This includes a good comprehension of provisioning resources, adjusting pipelines, monitoring pipelines, as well as testing & quality control.

Build & Operationalize Data Processing Systems

  • Build & Operationalize Processing Infrastructure: The considerations for this subject area include provisioning resources, adjusting pipeline, monitoring pipeline, and testing & quality control.
  • Build & Operationalize Storage Systems: This part will require the students’ skills and competence in the effective usage of managed services, including Cloud Spanner, CLoug Bigtable, BigQuery, Cloud SQL, Cloud Memorystore, Cloud Datastore, and Cloud Storage. It also covers their skills in managing the data lifecycle and storage performance and costs;
  • Build & Operationalize Pipeline: This module requires that the learners demonstrate competence in data cleansing, transformation, batch & streaming, data import & acquisition, as well as integration with the new data sources;

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

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

SectionWeightObjectives
Designing data processing systems20%- Designing for regulatory and security requirements
  • 1. Implementing access control and data protection
  • 2. Ensuring data privacy and compliance
- Designing for business requirements
  • 1. Designing for scalability and elasticity
  • 2. Designing for reliability and fault tolerance
  • 3. Selecting appropriate storage solutions
Ensuring solution quality and reliability17%- Testing and validating data systems
  • 1. Data quality validation
  • 2. Performance and scalability testing
- Troubleshooting and optimization
  • 1. Diagnosing performance issues
  • 2. Optimizing queries and workloads
Operationalizing machine learning models20%- Deploying and maintaining ML models
  • 1. Optimizing model performance and cost
  • 2. Model serving and monitoring
- Preparing data for ML
  • 1. Feature engineering and data preparation
  • 2. Handling structured and unstructured data
Maintaining and automating data workloads18%- Automation and repeatability
  • 1. Implementing CI/CD for data systems
  • 2. Automating deployment and updates
- Resource optimization
  • 1. Cost management and resource allocation
  • 2. Choosing appropriate compute and storage options
Building and operationalizing data processing systems25%- Building data pipelines
  • 1. Transforming and cleaning data
  • 2. Ingesting data from various sources
  • 3. Orchestrating data workflows
- Deploying and managing systems
  • 1. Managing infrastructure and resources
  • 2. Monitoring and logging data processes

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