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Google GCP-DE Exam Syllabus Topics:

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

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                                  GCP-DE Exam Questions Answers & GCP-DE Latest Test Pdf

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                                  Google Data Engineer Sample Questions (Q72-Q77):

                                  NEW QUESTION # 72
                                  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: C


                                  NEW QUESTION # 73
                                  When a Cloud Bigtable node fails, is lost.

                                  Answer: B

                                  Explanation:
                                  A Cloud Bigtable table is sharded into blocks of contiguous rows, called tablets, to help balance the workload of queries. Tablets are stored on Colossus, Google's file system, in SSTable format. Each tablet is associated with a specific Cloud Bigtable node.
                                  Data is never stored in Cloud Bigtable nodes themselves; each node has pointers to a set of tablets that are stored on Colossus. As a result:
                                  Rebalancing tablets from one node to another is very fast, because the actual data is not copied. Cloud Bigtable simply updates the pointers for each node.
                                  Recovery from the failure of a Cloud Bigtable node is very fast, because only metadata needs to be migrated to the replacement node.
                                  When a Cloud Bigtable node fails, no data is lost Reference: https://cloud.google.com/bigtable/docs/overview


                                  NEW QUESTION # 74
                                  You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

                                  Answer: B


                                  NEW QUESTION # 75
                                  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 dat a. Which two actions should you take? (Choose two.)

                                  Answer: B,D


                                  NEW QUESTION # 76
                                  Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

                                  Answer: D

                                  Explanation:
                                  When collecting and grouping data into windows, Beam uses triggers to determine when to emit the aggregated results of each window.
                                  Processing time triggers. These triggers operate on the processing time - the time when the data element is processed at any given stage in the pipeline. Event time triggers. These triggers operate on the event time, as indicated by the timestamp on each data element. Beam's default trigger is event time-based.
                                  Reference: https://beam.apache.org/documentation/programming-guide/#triggers


                                  NEW QUESTION # 77
                                  ......

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