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Understanding functional and technical aspects of Google Professional Data Engineer Exam Designing data processing systems

The following will be discussed here:

• Online (interactive) vs. batch predictions
• Mapping storage systems to business requirements
• Designing data processing systems
• Capacity planning
• System availability and fault tolerance
• Hybrid cloud and edge computing
• Tradeoffs involving latency, throughput, transactions
• Use of distributed systems
• Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)
• Designing data pipelines
• Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka)
• Data publishing and visualization (e.g., BigQuery)
• Selecting the appropriate storage technologies
• At least once, in-order, and exactly once, etc., event processing
• Data modeling
• Job automation and orchestration (e.g., Cloud Composer)
• Choice of infrastructure
• Distributed systems
• Schema design

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Google Professional-Data-Engineer日本語 Exam Syllabus Topics:

Reference: https://cloud.google.com/certification/data-engineer

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What is the duration, language, and format of Google Professional Data Engineer Exam

• Format: Multiple choices, multiple answers
• Passing score: 80%
• Length of Examination: 120 minutes
• Number of Questions: 50-60
• Language: English (U.S.), Japanese, Spanish, and Portuguese
• Cost: $200