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

Professional-Data-Engineer日本語
  • Exam Code: Professional-Data-Engineer-JPN
  • Exam Name: Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)
  • Updated: Jul 31, 2026
  • Q & A: 433 Questions and Answers
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Target Audience

The candidates for this certification are the data engineers or those aiming to become one. These individuals should have the capacity to allow data-driven decision-making through the collection, transformation, and publishing of data. They have the expertise in designing, building, and operationalizing secure data processing systems and monitoring the same. This is with the specific emphasis on compliance and security, fidelity and reliability, portability and flexibility, as well as efficiency and scalability.

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The benefit of obtaining the Google Professional Data Engineer Exam Certification

A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A data engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A data engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.

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Ensure Solution Quality

  • Ensure Portability & Flexibility: The considerations for this domain include the design for application and data portability, including data residency prerequisites and Multiple-Cloud. It also coves data staging, discovery, and cataloging, as well as mapping to future and current business prerequisites.
  • Design for Compliance & Security: The consideration for this topic includes identity & access management such as Cloud IAM. You should also know about data security (including key management and encryption) and privacy assurance (such as Data Loss Prevention API). This part also covers the skills needed in legal compliance, including Health Insurance Portability & Accountability Act, FedRAMP, Children’s Online Privacy Protection Act, and General Data Protection Regulation;
  • Ensure Efficiency & Scalability: The potential candidates will be required to demonstrate their ability to build and run test suits as well as monitor pipeline, including Stackdriver. It also focuses on their skills related to assessing, improving, and troubleshooting data process infrastructure and data representations. This area will also require that the test takers demonstrate the capacity to resize and autoscale resources;
  • Ensure Fidelity & Reliability: The applicants should be able to carry out data preparation & quality control (such as Cloud Dataprep), verify and monitor, as well as plan, execute, and stress test data recovery (including rerunning failed jobs, fault tolerance, and retrospective re-analysis performance). Besides that, they should be able to choose between idempotent ACID and eventual consistent prerequisites;

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

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

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

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