試験の準備方法-便利なGCP-DEトレーニング費用試験-認定するGCP-DE専門知識

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Google GCP-DE Exam Syllabus Topics:

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

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                                  試験の準備方法-素晴らしいGCP-DEトレーニング費用試験-効果的なGCP-DE専門知識

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                                  Google Data Engineer 認定 GCP-DE 試験問題 (Q37-Q42):

                                  質問 # 37
                                  Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer dat a. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?

                                  正解：C


                                  質問 # 38
                                  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?

                                  正解：D


                                  質問 # 39
                                  By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

                                  正解：C

                                  解説：
                                  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


                                  質問 # 40
                                  You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket. The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing dat a. Which Stackdriver alerts should you create?

                                  正解：D


                                  質問 # 41
                                  Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

                                  正解：A、B、C

                                  解説：
                                  Cloud Dataproc provides out-of-the box and end-to-end support for many of the most popular job types, including Spark, Spark SQL, PySpark, MapReduce, Hive, and Pig jobs.
                                  Reference: https://cloud.google.com/dataproc/docs/resources/faq#what_type_of_jobs_can_i_run


                                  質問 # 42
                                  ......

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