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

  • Exam Code: Professional-Data-Engineer日本語
  • Exam Name: Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)
  • Updated: Sep 14, 2026
  • Q & A: 433 Questions and Answers

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

Certification Path

The Google Professional Data Engineer Certification is one of the highest level of certification mainly focussing to the professional Data Engineering.

There is no prerequisite for this exam but still it would be best to follow some sequence in order to prove immense knowledge as a Google professional Data Engineer.

You can complete Google Associate Certifications then approach for the professional certification. For more information related to Google cloud certification track Google-certification-path

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

Training Courses Recommended for the Exam Preparation

Training courses are meant to help candidates to learn about the Google exam syllabus and prepare well. It has hands-on labs and expert support that will allow you to get in-depth knowledge of each domain covered in the test. So, these are some of the best training courses offered by Google for the Professional Data Engineer certification exam.

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What is the duration, language, and format of Google Professional Data Engineer Exam

  • Number of Questions: 50-60
  • Cost: $200
  • Format: Multiple choices, multiple answers
  • Length of Examination: 120 minutes
  • Language: English (U.S.), Japanese, Spanish, and Portuguese
  • Passing score: 80%

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Who is the Professional Data Engineer Exam Intended for?

This exam is designed for individuals who are experts in designing, building, securing, and monitoring data processing systems with a particular emphasis on compliance and security. The candidate who wants to take the Professional Data Engineer exam should have the ability to deploy, leverage, and training pre-existing machine learning models. Moreover, every applicant should have experience of more than 3 years including 1-year experience in designing and handling solutions utilizing GCP.

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

SectionWeightObjectives
Maintaining and automating data workloads (~15% of the exam)15%- Monitoring data pipelines and data processes
  • 1. Logging, monitoring, and troubleshooting
  • 2. Managing quotas and resource usage
- Automating data processes
  • 1. Scheduling jobs
  • 2. Continuous integration and continuous deployment (CI/CD)
  • 3. Workflow orchestration
- Designing for reliability and fidelity
  • 1. Performing data quality and validation checks
  • 2. Recovering from failures
  • 3. Planning for monitoring and alerting
Storing the data (~20% of the exam)20%- Selecting storage systems
  • 1. Planning for storage costs and performance
  • 2. Lifecycle management of data
  • 3. Analyzing data access patterns
- Using a data lake
  • 1. Monitoring the data lake
  • 2. Processing data
  • 3. Managing the lake (data discovery, access, cost controls)
- Planning for using a data warehouse
  • 1. Defining architecture to support data access patterns
  • 2. Deciding the degree of data normalization
  • 3. Mapping business requirements
  • 4. Designing the data model
- Designing for a data platform
  • 1. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage
  • 2. Building a federated governance model for distributed data systems
Designing data processing systems (~30% of the exam)30%- Designing data processing resources
  • 1. Cost optimization
  • 2. Cluster sizing and autoscaling
  • 3. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
- Designing data pipelines
  • 1. Integrating with new data sources
  • 2. AI data enrichment
  • 3. Batch processing
  • 4. Processing logic
  • 5. Data acquisition and import
  • 6. Streaming (e.g., windowing, late arriving data)
- Selecting appropriate storage technologies
  • 1. Mapping storage options to business requirements
  • 2. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB
Preparing and using data for analysis (~15% of the exam)15%- Preparing data for visualization
  • 1. Connecting to Looker and other BI tools
  • 2. Preparing data for reporting and dashboards
- Sharing data securely
  • 1. Data sharing and collaboration
  • 2. Publishing datasets
Ingesting and processing the data (~20% of the exam)20%- Building and maintaining data structures and databases
  • 1. Planning for analytical and operational use cases
  • 2. Defining data lifecycle
- Deploying and operationalizing the pipelines
  • 1. Job automation and orchestration (Cloud Composer, Workflows)
  • 2. CI/CD for data pipelines
- Performing security considerations
  • 1. Data encryption
  • 2. Auditing, privacy, and compliance
  • 3. Identity and Access Management (IAM)

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