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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. ETL/ELT workflows
        • 2. Pub/Sub streaming ingestion
          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
                  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
                          Operationalizing data and ML pipelines30%- Monitoring and troubleshooting
                          • 1. Performance optimization and debugging
                            • 2. Logging and observability
                              - Pipeline automation and orchestration
                              • 1. Scheduling and monitoring pipelines
                                • 2. Cloud Composer workflows

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                                  最新的 Google Cloud Certified GCP-DE 免費考試真題 (Q58-Q63):

                                  問題 #58
                                  Which of the following is NOT true about Dataflow pipelines?

                                  答案:B

                                  解題說明:
                                  Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs Reference: https://cloud.google.com/dataflow/


                                  問題 #59
                                  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.)

                                  答案:D,E


                                  問題 #60
                                  For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

                                  答案:D

                                  解題說明:
                                  It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance, If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
                                  It is recommended to avoid creating your Compute Engine instance in a different region from your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.
                                  Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance


                                  問題 #61
                                  You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?

                                  答案:D


                                  問題 #62
                                  You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

                                  答案:C


                                  問題 #63
                                  ......

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