GCP-DE日本語学習内容 & GCP-DE独学書籍

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Google GCP-DE Exam Syllabus Topics:

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

                                  >> GCP-DE日本語学習内容 <<

                                  Google GCP-DE日本語学習内容: Data Engineer - Fast2test 役立つヒントと質問

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                                  Google Data Engineer 認定 GCP-DE 試験問題 (Q30-Q35):

                                  質問 # 30
                                  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?

                                  正解：D

                                  解説：
                                  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


                                  質問 # 31
                                  You architect a system to analyze seismic dat
                                  a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

                                  正解：B


                                  質問 # 32
                                  Cloud Dataproc is a managed Apache Hadoop and Apache service.

                                  正解：B

                                  解説：
                                  Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.
                                  Reference: https://cloud.google.com/dataproc/docs/


                                  質問 # 33
                                  If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

                                  正解：A

                                  解説：
                                  If you're running a performance test that depends upon Cloud Bigtable, be sure to follow these steps as you plan and execute your test:
                                  Use a production instance. A development instance will not give you an accurate sense of how a production instance performs under load.
                                  Use at least 300 GB of data. Cloud Bigtable performs best with 1 TB or more of data. However, 300 GB of data is enough to provide reasonable results in a performance test on a 3-node cluster. On larger clusters, use 100 GB of data per node.
                                  Before you test, run a heavy pre-test for several minutes. This step gives Cloud Bigtable a chance to balance data across your nodes based on the access patterns it observes.
                                  Run your test for at least 10 minutes. This step lets Cloud Bigtable further optimize your data, and it helps ensure that you will test reads from disk as well as cached reads from memory.
                                  Reference: https://cloud.google.com/bigtable/docs/performance


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

                                  正解：B

                                  解説：
                                  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


                                  質問 # 35
                                  ......

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