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Updated: Aug 01, 2026

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How to Prepare For Google Professional Data Engineer Exam

Preparation Guide for Google Professional Data Engineer Exam

Introduction to Google Professional Data Engineer Exam

Google has established a path for IT professionals endorse as a Data Engineer on the GCP platform. This accreditation program gives Google cloud professionals a way to endorse their skills. The evaluation relies on a meticulous exam using industry standard methodology to conclude whether or not a aspirant meets Google's proficiency standards.

The Professional Data Engineer exam assesses your ability to:

  • Design data processing systems
  • Build and operationalize data processing systems
  • Operationalize machine learning models
  • Ensure solution quality

Google Professional Data Engineer Exam certification is evidence of your skills, expertise in those areas in which you like to work. If candidate wants to work on Google Professional Data Engineer and prove his knowledge, Certification offered by Google. This Google Professional Data Engineer Certification helps a candidate to validates his skills in Big Data and Data engineering Technology.

Google Cloud Big Data & Machine Learning Fundamentals course

This course is a gateway to introduce you to Google Cloud's big data and different machine learning functions. However, to successfully pass this training, you have to attain one year of experience in SQL, extract transform, data modeling, machine learning, programming in Python, and load activities. So, the objectives of the course are the following:

  • Hire BigQuery and Cloud SQL for interactive data analysis
  • Utilize Cloud SQL & Dataproc to migrate existing MySQL, Pig, Spark, or Hive workloads to Google Cloud
  • Recognize the purpose of the key Big data and Machine Learning products in Google Cloud
  • Create ML models using BigQuery ML, APIs, and AutoML.

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

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

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

SectionWeightObjectives
Topic 1: Operationalizing machine learning models26%- ML pipeline integration
  • 1. Feature engineering and feature stores
    • 2. Vertex AI pipeline deployment
      - Model deployment and monitoring
      • 1. Online vs batch prediction
        • 2. Model monitoring and drift detection
          Topic 2: Designing data processing systems22%- Data architecture and storage design
          • 1. Designing scalable and cost-effective data models
            • 2. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)
              - Batch and streaming data processing design
              • 1. Event-driven vs batch architectures
                • 2. Latency, throughput, and consistency trade-offs
                  Topic 3: Ensuring solution quality28%- Reliability and performance
                  • 1. Fault tolerance and recovery strategies
                    • 2. Monitoring pipelines and workloads
                      - Security and governance
                      • 1. IAM and access control in GCP
                        • 2. Data governance and compliance
                          Topic 4: Building and operationalizing data processing systems24%- Data processing and transformation
                          • 1. Using Dataproc, Dataflow, and BigQuery SQL
                            • 2. ETL/ELT pipeline design
                              - Data ingestion and integration
                              • 1. Streaming ingestion (Pub/Sub, Dataflow)
                                • 2. Batch ingestion pipelines (BigQuery, Cloud Storage)

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