Google Certified Professional Data Engineer : Professional-Data-Engineer exam

Professional-Data-Engineer
  • Exam Code: Professional-Data-Engineer
  • Exam Name: Google Certified Professional Data Engineer Exam
  • Updated: Sep 16, 2026
  • Q & A: 433 Questions and Answers

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

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Completing the exam associated with the Google Professional Data Engineer certification provides you with a great validation of your skills in designing, building, operationalizing, securing, and monitoring data processing systems. The job roles that you can take up after getting certified include a Google Cloud Data Engineer, an Operations Engineer, a Cloud Infrastructure Engineer, a DevOps Infrastructure Engineer, a Cloud Database Engineer, a Google Cloud IAM Engineer, a DataOps Engineer, a Big Data Engineer, a Google Cloud Platform Data Architect, and more. The average salary that you can expect to earn with this certificate is around $125,550 per year. However, the real remuneration will depend on a specific job title, location of an individual, and his/her working experience.

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This course will show you how to manage big data including loading, extracting, cleaning, and validating data. At the end of the training, you can easily create machine learning and statistical models as well as visualizing query results. This program is a bit lengthy but you have to practice well to get the knowledge needed on the actual exam. These are the following modules covered in the course:

  • Handling Data Pipelines with Cloud Composer and Cloud Data Fusion
  • Custom Model building Using SQL in BigQuery ML
  • Introduction to Processing Streaming Data
  • Creating a Data Lake
  • Building a Data Warehouse
  • Performing Spark on Cloud Dataproc
  • Serverless Data Processing with Cloud Dataflow
  • Big Data Analytics with Cloud Al Platform Notebook
  • Serverless Messaging Using Cloud Sub/Pub
  • Introduction to Building Batch Data Pipelines
  • Advanced BigQuery Performance and Functionality
  • Introduction to Data Engineering
  • Cloud Dataflow Streaming Features
  • Production ML Pipelines and use of Kubeflow
  • Prebuilt ML Models APIs for Unsaturated Data
  • Bigtable Streaming Features and High-Throughput BigQuery
  • Custom Model building Utilizing Cloud AutoML

These modules involve everything the candidate requires for passing the Professional Data Engineer certification exam. Thus, you will not miss anything if you are taking this learning program keenly and apply the required knowledge in an appropriate way. You would end up getting a good score and achieving the Google Professional Data Engineer certification.

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

Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Ensuring solution quality28%- Reliability and performance
  • 1. Fault tolerance and recovery strategies
    • 2. Monitoring pipelines and workloads
      - Security and governance
      • 1. Data governance and compliance
        • 2. IAM and access control in GCP
          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
                  Designing data processing systems22%- Batch and streaming data processing design
                  • 1. Event-driven vs batch architectures
                    • 2. Latency, throughput, and consistency trade-offs
                      - Data architecture and storage design
                      • 1. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)
                        • 2. Designing scalable and cost-effective data models
                          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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