公認されたProfessional-Data-Engineer復習範囲 &資格試験のリーダー &高品質Professional-Data-Engineer: Google Certified Professional Data Engineer Exam

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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Ensuring solution quality28%- Reliability and performance
  • 1. Monitoring pipelines and workloads
    • 2. Fault tolerance and recovery strategies
      - Security and governance
      • 1. IAM and access control in GCP
        • 2. Data governance and compliance
          Topic 2: Building and operationalizing data processing systems24%- Data processing and transformation
          • 1. Using Dataproc, Dataflow, and BigQuery SQL
            • 2. ETL/ELT pipeline design
              - Data ingestion and integration
              • 1. Batch ingestion pipelines (BigQuery, Cloud Storage)
                • 2. Streaming ingestion (Pub/Sub, Dataflow)
                  Topic 3: Operationalizing machine learning models26%- ML pipeline integration
                  • 1. Feature engineering and feature stores
                    • 2. Vertex AI pipeline deployment
                      - Model deployment and monitoring
                      • 1. Online vs batch prediction
                        • 2. Model monitoring and drift detection
                          Topic 4: Designing data processing systems22%- Data architecture and storage design
                          • 1. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)
                            • 2. Designing scalable and cost-effective data models
                              - Batch and streaming data processing design
                              • 1. Latency, throughput, and consistency trade-offs
                                • 2. Event-driven vs batch architectures

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                                  Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q257-Q262):

                                  質問 # 257
                                  By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

                                  正解：D

                                  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 # 258
                                  Your financial services company has a critical daily reconciliation process that involves several distinct steps: fetching data from an external SFTP server, decrypting the files, loading them into Cloud Storage, and finally running a series of BigQuery SQL transformations. Each step has strict dependencies, and the entire process should notify you if not completed by 7:00 AM. Manual intervention for failures is costly and delays compliance reporting. You need a highly observable and robust solution that supports easy re-runs of individual steps if errors occur. What should you do?

                                  正解：A

                                  解説：
                                  Defining each step as a separate task in a Cloud Composer DAG provides fine-grained dependency management, built-in observability, and clear task-level monitoring. This approach allows individual steps to be retried or re-run independently, supports robust failure handling and alerting for SLA breaches such as missing the 7:00 AM deadline, and aligns with best practices for orchestrating complex, dependent data pipelines.


                                  質問 # 259
                                  Your software uses a simple JSON format for all messages. These messages are published to Google Cloud Pub/Sub, then processed with Google Cloud Dataflow to create a real-time dashboard for the CFO. During testing, you notice that some messages are missing in the dashboard. You check the logs, and all messages are being published to Cloud Pub/Sub successfully. What should you do next?

                                  正解：D

                                  解説：
                                  Explanation:


                                  質問 # 260
                                  Your company receives both batch- and stream-based event data. You want to process the data using
                                  Google Cloud Dataflow over a predictable time period. However, you realize that in some instances data
                                  can arrive late or out of order. How should you design your Cloud Dataflow pipeline to handle data that is
                                  late or out of order?

                                  正解：A


                                  質問 # 261
                                  You are building an application to share financial market data with consumers, who will receive data feeds. Data is collected from the markets in real time. Consumers will receive the data in the following ways:
                                  - Real-time event stream
                                  - ANSI SQL access to real-time stream and historical data
                                  - Batch historical exports
                                  Which solution should you use?

                                  正解：B


                                  質問 # 262
                                  ......

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