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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Building and operationalizing data processing systems30%- Data pipeline implementation
  • 1. Dataflow pipeline development
    • 2. Dataproc and Spark-based processing
      - Data ingestion and transformation
      • 1. ETL/ELT workflows
        • 2. Pub/Sub streaming ingestion
          Operationalizing data and ML pipelines30%- Pipeline automation and orchestration
          • 1. Cloud Composer workflows
            • 2. Scheduling and monitoring pipelines
              - Monitoring and troubleshooting
              • 1. Performance optimization and debugging
                • 2. Logging and observability
                  Maintaining and optimizing data and ML solutions20%- Machine learning integration
                  • 1. BigQuery ML usage
                    • 2. Vertex AI integration for pipelines
                      - Security and governance
                      • 1. IAM and access control
                        • 2. Data encryption and compliance
                          Designing data processing systems20%- Data pipeline architecture design
                          • 1. Batch vs streaming data processing selection
                            • 2. Scalable data ingestion design
                              - Storage and data modeling
                              • 1. Data lake architecture on Google Cloud Storage
                                • 2. Data warehouse design using BigQuery

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                                  Google Data Engineer Sample Questions (Q33-Q38):

                                  NEW QUESTION # 33
                                  An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
                                  They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

                                  Answer: D


                                  NEW QUESTION # 34
                                  All Google Cloud Bigtable client requests go through a front-end server they are sent to a Cloud Bigtable node.

                                  Answer: D

                                  Explanation:
                                  In a Cloud Bigtable architecture all client requests go through a front-end server before they are sent to a Cloud Bigtable node.
                                  The nodes are organized into a Cloud Bigtable cluster, which belongs to a Cloud Bigtable instance, which is a container for the cluster. Each node in the cluster handles a subset of the requests to the cluster.
                                  When additional nodes are added to a cluster, you can increase the number of simultaneous requests that the cluster can handle, as well as the maximum throughput for the entire cluster.
                                  Reference: https://cloud.google.com/bigtable/docs/overview


                                  NEW QUESTION # 35
                                  You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

                                  Answer: D


                                  NEW QUESTION # 36
                                  What are two methods that can be used to denormalize tables in BigQuery?

                                  Answer: C

                                  Explanation:
                                  The conventional method of denormalizing data involves simply writing a fact, along with all its dimensions, into a flat table structure. For example, if you are dealing with sales transactions, you would write each individual fact to a record, along with the accompanying dimensions such as order and customer information. The other method for denormalizing data takes advantage of BigQuery's native support for nested and repeated structures in JSON or Avro input data. Expressing records using nested and repeated structures can provide a more natural representation of the underlying data. In the case of the sales order, the outer part of a JSON structure would contain the order and customer information, and the inner part of the structure would contain the individual line items of the order, which would be represented as nested, repeated elements.
                                  Reference: https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data


                                  NEW QUESTION # 37
                                  Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

                                  Answer: C


                                  NEW QUESTION # 38
                                  ......

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