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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.

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
Related Certifications:Google Cloud Certified - Associate Cloud Engineer
Google Cloud Certified - Professional Machine Learning Engineer
Google Cloud Certified - Professional Cloud Architect
Google Cloud Certified - Professional Data Analytics Engineer
Exam Duration:120 minutes
Passing Score:Not publicly disclosed (scaled scoring system)
Available Languages:Spanish, Japanese, German, French, Portuguese, English
Certificate Validity Period:2 years
Exam Format:multiple choice, case study, multiple select
Real Exam Qty:Approximately 50–60 multiple choice and multiple select questions
Exam Price:$200 USD
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

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

Exam Topics

The syllabus of the Google Professional Data Engineer exam is divided into 4 topics, each covering specific knowledge and skills that the candidates need to develop while preparing for the test. A full outline of the exam content can be viewed on the official website. The highlights of the domains covered in the test are as follows:

Topic 1. Designing Data Processing Systems

To answer the questions related to this first topic of the certification exam, the individuals need to demonstrate their proficiency in selecting the proper storage technologies. This includes their understanding of data modeling, schema design, distributed systems, as well as tradeoffs involving throughput, latency, and transactions. Moreover, the applicants need to have the ability to map storage systems to the business needs. It also measures one’s skills in designing data pipelines, designing a data processing solution, as well as migrating data warehousing & data processing.

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

SectionWeightObjectives
Building and operationalizing data processing systems24%- Data ingestion and integration
  • 1. Streaming ingestion (Pub/Sub, Dataflow)
    • 2. Batch ingestion pipelines (BigQuery, Cloud Storage)
      - Data processing and transformation
      • 1. ETL/ELT pipeline design
        • 2. Using Dataproc, Dataflow, and BigQuery SQL
          Operationalizing machine learning models26%- Model deployment and monitoring
          • 1. Online vs batch prediction
            • 2. Model monitoring and drift detection
              - ML pipeline integration
              • 1. Feature engineering and feature stores
                • 2. Vertex AI pipeline deployment
                  Ensuring solution quality28%- Security and governance
                  • 1. Data governance and compliance
                    • 2. IAM and access control in GCP
                      - Reliability and performance
                      • 1. Fault tolerance and recovery strategies
                        • 2. Monitoring pipelines and workloads
                          Designing data processing systems22%- Batch and streaming data processing design
                          • 1. Latency, throughput, and consistency trade-offs
                            • 2. Event-driven vs batch architectures
                              - Data architecture and storage design
                              • 1. Designing scalable and cost-effective data models
                                • 2. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)

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