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The Google Professional-Data-Engineer-JPN exam consists of multiple-choice and scenario-based questions that test the candidates' understanding of GCP data engineering services and best practices for data engineering. Candidates have two hours and thirty minutes to complete the exam. Professional-Data-Engineer-JPN exam is available in English, Japanese, Spanish, and Portuguese.

Passing the Google Professional-Data-Engineer Certification Exam is a significant achievement that can lead to multiple career opportunities. It can help individuals establish themselves as experts in the field of data engineering and demonstrate their ability to design, implement, and manage complex data processing systems. Moreover, it can also increase their credibility and earning potential, as the demand for certified data engineers is on the rise. Therefore, if you are looking to establish a career in data engineering, obtaining the Google Professional-Data-Engineer Certification is a must.

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

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Professional Data Engineer Exam Details

Like other Google exams, this exam also consists of multiple choice and multiple select questions. Consider the fact that you need to pay $200 for the registration. After that, you will access the test for 2 hours which is presented either in English or Japanese. Moreover, you can either take the exam online or have to find a test center near your place to take this test.

There is no formal prerequisite for the exam but it is recommended to have 3-4 years of experience within the data engineering field and to be responsible for the tasks related to data engineering and machine learning. So, on the final test day, you need to have exhaustive knowledge about these domains to perform your best.

  • Building data processing systems
  • Designing data processing systems
  • Providing solution quality
  • Operationalizing machine learning models

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

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

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