Google Professional-Data-Engineer Exam Details - Exam Professional-Data-Engineer Torrent

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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. Designing scalable and cost-effective data models
        • 2. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)
          Topic 2: 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 3: Operationalizing machine learning models26%- Model deployment and monitoring
                  • 1. Online vs batch prediction
                    • 2. Model monitoring and drift detection
                      - ML pipeline integration
                      • 1. Vertex AI pipeline deployment
                        • 2. Feature engineering and feature stores
                          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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                                  Free PDF Quiz 2026 Professional Professional-Data-Engineer: Google Certified Professional Data Engineer Exam Exam Details

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                                  Google Certified Professional Data Engineer Exam Sample Questions (Q357-Q362):

                                  NEW QUESTION # 357
                                  You are collecting IoT sensor data from millions of devices across the world and storing the data in BigQuery. Your access pattern is based on recent data, filtered by location_id and device_version with the following query:

                                  You want to optimize your queries for cost and performance. How should you structure your data?

                                  Answer: B


                                  NEW QUESTION # 358
                                  Which is the preferred method to use to avoid hotspotting in time series data in Bigtable?

                                  Answer: A

                                  Explanation:
                                  By default, prefer field promotion. Field promotion avoids hotspotting in almost all cases, and it tends to make it easier to design a row key that facilitates queries.
                                  Reference: https://cloud.google.com/bigtable/docs/schema-design-time-
                                  series#ensure_that_your_row_key_avoids_hotspotting


                                  NEW QUESTION # 359
                                  An organization maintains a Google BigQuery dataset that contains tables with user-level data. They want to expose aggregates of this data to other Google Cloud projects, while still controlling access to the user- level data. Additionally, they need to minimize their overall storage cost and ensure the analysis cost for other projects is assigned to those projects. What should they do?

                                  Answer: A

                                  Explanation:
                                  Explanation/Reference:
                                  Reference: https://cloud.google.com/bigquery/docs/access-control


                                  NEW QUESTION # 360
                                  You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the data. Which two actions should you take? (Choose two.)

                                  Answer: B,E

                                  Explanation:
                                  References:


                                  NEW QUESTION # 361
                                  The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster
                                  ____.

                                  Answer: A

                                  Explanation:
                                  Explanation
                                  The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster master node. The cluster master-host-name is the name of your Cloud Dataproc cluster followed by an -m suffix-for example, if your cluster is named "my-cluster", the master-host-name would be "my-cluster-m".
                                  Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces


                                  NEW QUESTION # 362
                                  ......

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