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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
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. Batch ingestion pipelines (BigQuery, Cloud Storage)
        • 2. Streaming ingestion (Pub/Sub, Dataflow)
          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
                  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
                          Ensuring solution quality28%- Security and governance
                          • 1. Data governance and compliance
                            • 2. IAM and access control in GCP
                              - Reliability and performance
                              • 1. Monitoring pipelines and workloads
                                • 2. Fault tolerance and recovery strategies

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                                  Google Certified Professional Data Engineer Exam Sample Questions (Q38-Q43):

                                  NEW QUESTION # 38
                                  You have a job that you want to cancel. It is a streaming pipeline, and you want to ensure that any data that is in-flight is processed and written to the output. Which of the following commands can you use on the Dataflow monitoring console to stop the pipeline job?

                                  Answer: C

                                  Explanation:
                                  Using the Drain option to stop your job tells the Dataflow service to finish your job in its current state. Your job will immediately stop ingesting new data from input sources, but the Dataflow service will preserve any existing resources (such as worker instances) to finish processing and writing any buffered data in your pipeline.
                                  Reference:
                                  https://cloud.google.com/dataflow/pipelines/stopping-a-pipeline


                                  NEW QUESTION # 39
                                  You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics.
                                  Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?

                                  Answer: A

                                  Explanation:
                                  Explanation


                                  NEW QUESTION # 40
                                  By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

                                  Answer: A

                                  Explanation:
                                  Dataflow's default windowing behavior is to assign all elements of a PCollection to a single, global window, even for unbounded PCollections Reference: https://cloud.google.com/dataflow/model/pcollection


                                  NEW QUESTION # 41
                                  Which of the following is not true about Dataflow pipelines?

                                  Answer: D

                                  Explanation:
                                  The data and transforms in a pipeline are unique to, and owned by, that pipeline. While your program can create multiple pipelines, pipelines cannot share data or transforms


                                  NEW QUESTION # 42
                                  You need to modernize your existing on-premises data strategy. Your organization currently uses.
                                  * Apache Hadoop clusters for processing multiple large data sets, including on-premises Hadoop Distributed File System (HDFS) for data replication.
                                  * Apache Airflow to orchestrate hundreds of ETL pipelines with thousands of job steps.
                                  You need to set up a new architecture in Google Cloud that can handle your Hadoop workloads and requires minimal changes to your existing orchestration processes. What should you do?

                                  Answer: B

                                  Explanation:
                                  Dataproc is a fully managed service that allows you to run Apache Hadoop and Spark workloads on Google Cloud. It is compatible with the open source ecosystem, so you can migrate your existing Hadoop clusters to Dataproc with minimal changes. Cloud Storage is a scalable, durable, and cost-effective object storage service that can replace HDFS for storing and accessing data. Cloud Storage offers interoperability with Hadoop through connectors, so you can use it as a data source or sink for your Dataproc jobs. Cloud Composer is a fully managed service that allows you to create, schedule, and monitor workflows using Apache Airflow. It is integrated with Google Cloud services, such as Dataproc, BigQuery, Dataflow, and Pub/Sub, so you can orchestrate your ETL pipelines across different platforms. Cloud Composer is compatible with your existing Airflow code, so you can migrate your existing orchestration processes to Cloud Composer with minimal changes.
                                  The other options are not as suitable as Dataproc and Cloud Composer for this use case, because they either require more changes to your existing code, or do not meet your requirements. Dataflow is a fully managed service that allows you to create and run scalable data processing pipelines using Apache Beam. However, Dataflow is not compatible with your existing Hadoop code, so you would need to rewrite your ETL pipelines using Beam. Bigtable is a fully managed NoSQL database service that can handle large and complex data sets.
                                  However, Bigtable is not compatible with your existing Hadoop code, so you would need to rewrite your queries and applications using Bigtable APIs. Cloud Data Fusion is a fully managed service that allows you to visually design and deploy data integration pipelines using a graphical interface. However, Cloud Data Fusion is not compatible with your existing Airflow code, so you would need to recreate your orchestration processes using Cloud Data Fusion UI. References:
                                  * Dataproc overview
                                  * Cloud Storage connector for Hadoop
                                  * Cloud Composer overview


                                  NEW QUESTION # 43
                                  ......

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