Google Professional-Data-Engineer : Google Certified Professional Data Engineer Exam

  • Exam Code: Professional-Data-Engineer
  • Exam Name: Google Certified Professional Data Engineer Exam
  • Updated: Aug 01, 2026
  • Q & A: 433 Questions and Answers

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About Google Professional-Data-Engineer Exam

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Google Professional Data Engineer Certified Professional salary

The average salary of a Google Professional Data Engineer Certified Expert in

  • India - 25,42,327 INR
  • Europe - 135,347 EURO
  • England - 115,632 POUND
  • United State - 151,247 USD

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

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.

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

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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:

  • Create ML models using BigQuery ML, APIs, and AutoML.
  • Recognize the purpose of the key Big data and Machine Learning products in Google Cloud
  • 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

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

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 Professional-Data-Engineer 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. Model monitoring and drift detection
        • 2. Online vs batch prediction
          Topic 2: 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. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)
                • 2. Designing scalable and cost-effective data models
                  Topic 3: 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. Using Dataproc, Dataflow, and BigQuery SQL
                        • 2. ETL/ELT pipeline design
                          Topic 4: 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

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