Professional-Data-Engineer Questions - Pass On First Try [2026]

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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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                                  Google Certified Professional Data Engineer Exam Sample Questions (Q314-Q319):

                                  NEW QUESTION # 314
                                  You are designing the architecture of your application to store data in Cloud Storage. Your application consists of pipelines that read data from a Cloud Storage bucket that contains raw data, and write the data to a second bucket after processing. You want to design an architecture with Cloud Storage resources that are capable of being resilient if a Google Cloud regional failure occurs. You want to minimize the recovery point objective (RPO) if a failure occurs, with no impact on applications that use the stored data. What should you do?

                                  Answer: A

                                  Explanation:
                                  To ensure resilience and minimize the recovery point objective (RPO) with no impact on applications, using a dual-region bucket with turbo replication is the best approach. Here's why option D is the best choice:
                                  * Dual-Region Buckets:
                                  * Dual-region buckets store data redundantly across two distinct geographic regions, providing high availability and durability.
                                  * This setup ensures that data remains available even if one region experiences a failure.
                                  * Turbo Replication:
                                  * Turbo replication ensures that data is replicated between the two regions within 15 minutes, aligning with the requirement to minimize the recovery point objective (RPO).
                                  * This feature provides near real-time replication, significantly reducing the risk of data loss.
                                  * No Impact on Applications:
                                  * Applications continue to access the dual-region bucket without any changes, ensuring seamless operation even during a regional failure.
                                  * The dual-region setup transparently handles failover, providing uninterrupted access to data.
                                  Steps to Implement:
                                  * Create a Dual-Region Bucket:
                                  * Create a dual-region Cloud Storage bucket in the Google Cloud Console, selecting appropriate regions (e.g., us-central1 and us-east1).
                                  * Enable Turbo Replication:
                                  * Enable turbo replication to ensure rapid data replication between the selected regions.
                                  * Configure Applications:
                                  * Ensure that applications read and write to the dual-region bucket, benefiting from its high availability and durability.
                                  * Test Failover:
                                  * Simulate a regional failure to verify that the dual-region bucket and turbo replication meet the required RPO and ensure data resilience.
                                  Reference Links:
                                  * Google Cloud Storage Dual-Region
                                  * Turbo Replication in Google Cloud Storage


                                  NEW QUESTION # 315
                                  Which of these sources can you not load data into BigQuery from?

                                  Answer: B

                                  Explanation:
                                  You can load data into BigQuery from a file upload, Google Cloud Storage, Google Drive, or Google Cloud Bigtable. It is not possible to load data into BigQuery directly from Google Cloud SQL. One way to get data from Cloud SQL to BigQuery would be to export data from Cloud SQL to Cloud Storage and then load it from there.
                                  Reference: https://cloud.google.com/bigquery/loading-data


                                  NEW QUESTION # 316
                                  Which role must be assigned to a service account used by the virtual machines in a Dataproc cluster so they can execute jobs?

                                  Answer: D

                                  Explanation:
                                  Service accounts used with Cloud Dataproc must have Dataproc/Dataproc Worker role (or have all the permissions granted by Dataproc Worker role).


                                  NEW QUESTION # 317
                                  Your software uses a simple JSON format for all messages. These messages are published to Google Cloud Pub/Sub, then processed with Google Cloud Dataflow to create a real-time dashboard for the CFO.
                                  During testing, you notice that some messages are missing in the dashboard. You check the logs, and all messages are being published to Cloud Pub/Sub successfully. What should you do next?

                                  Answer: D

                                  Explanation:
                                  Stackdriver can be used to check the error like number of unack messages, publisher pushing messages faster.


                                  NEW QUESTION # 318
                                  Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub streaming data, one of the important business requirements is to be able to periodically identify the inputs and their timings during their campaign. Engineers have decided to use windowing and transformation in Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud Dataflow job fails for the all streaming insert. What is the most likely cause of this problem?

                                  Answer: C


                                  NEW QUESTION # 319
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