Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版): Professional-Data-Engineer日本語 Exam Premium File as PDF & Test Engine

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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:

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

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

Google Cloud Big Data & Machine Learning Fundamentals course

This course is a gateway to introduce you to Google Cloud's big data and different machine learning functions. However, to successfully pass this training, you have to attain one year of experience in SQL, extract transform, data modeling, machine learning, programming in Python, and load activities. So, the objectives of the course are the following:

• Hire BigQuery and Cloud SQL for interactive data analysis
• Create ML models using BigQuery ML, APIs, and AutoML.
• Recognize the purpose of the key Big data and Machine Learning products in Google Cloud
• Utilize Cloud SQL & Dataproc to migrate existing MySQL, Pig, Spark, or Hive workloads to Google Cloud

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