Google Professional-Data-Engineer日本語 Valid Test : Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)

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Operationalizing Machine Learning Models

Here the candidates need to demonstrate their expertise in using pre-built Machine Learning models as a service, including Machine Learning APIs (for instance, Speech API, Vision API, etc.), customizing Machine Learning APIs (for instance, Auto ML text, AutoML Vision, etc.), conversational experiences (for instance, Dialogflow). The applicants should also have the skills in deploying the Machine Learning pipeline. This involves the ability to ingest relevant data, perform retraining of machine learning models (BigQuery ML, Cloud Machine Learning Engine, Spark ML, Kubeflow), as well as execute continuous evaluation. Additionally, the students should be able to choose the relevant training & serving infrastructure as well as know how to fulfill measuring, monitoring, and troubleshooting of Machine Learning models.

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

The following will be discussed here:

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

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

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Requirements

The certification does not have any official prerequisites. However, it is advised to have at least three years of industry experience with one or more years of expertise in designing and managing different solutions with the use of Google Cloud Platform. It is also required to review the topics of the qualifying exam before sitting for it.

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