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

Professional-Data-Engineer Exam Questions
  • Exam Code: Professional-Data-Engineer
  • Exam Name: Google Certified Professional Data Engineer Exam
  • Updated: Oct 10, 2026
  • Q & A: 433 Questions and Answers
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About Google Professional-Data-Engineer Exam Braindumps

Building & Operationalizing Data Processing Systems

Within this subject area, the test takers should show that they know how to build and operationalize storage systems. Specifically, they need to be conversant with effective use of managed services (such as Cloud Bigtable, Cloud SQL, Cloud Spanner, BigQuery, Cloud Storage, Cloud Memorystore, Cloud Datastore), storage costs & performance, and lifecycle management of data. The students should also be capable of building as well as operationalizing pipelines, including such technical tasks as data cleansing, transformation, batch & streaming, data acquisition & import, and integrating with new data sources. Apart from that, the candidates need to have sufficient competency to build and operationalize the processing infrastructure. This includes a good comprehension of provisioning resources, adjusting pipelines, monitoring pipelines, as well as testing & quality control.

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Exam Details

The Google Professional Data Engineer certification exam has the duration of 2 hours. The qualifying test is made up of multiple-select and multiple-choice questions. The exam is available either in Japanese or English. To register for it, you are required to go through the official webpage and pay the fee of $200 plus applicable taxes. While completing the registration process, the potential individuals can choose the preferred method of exam delivery. It can be taken in person at the nearest testing center or online from a remote location.

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

Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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
          Topic 2: Building and operationalizing data processing systems24%- Data processing and transformation
          • 1. ETL/ELT pipeline design
            • 2. Using Dataproc, Dataflow, and BigQuery SQL
              - Data ingestion and integration
              • 1. Batch ingestion pipelines (BigQuery, Cloud Storage)
                • 2. Streaming ingestion (Pub/Sub, Dataflow)
                  Topic 3: Operationalizing machine learning models26%- ML pipeline integration
                  • 1. Vertex AI pipeline deployment
                    • 2. Feature engineering and feature stores
                      - Model deployment and monitoring
                      • 1. Model monitoring and drift detection
                        • 2. Online vs batch prediction
                          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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