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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:

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

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

Who should take the Google Professional Data Engineer exam

Individuals should pursue the exam if they want to demonstrate their expertise and ability to design and develop Data Engineering. Following professional get benefited from Google Professional Data Engineer Certification

• Data scientists
• Data analysts
• Data engineers
• Business analysts
• Data architects
• Developers responsible for managing big data transformation initiatives

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Exam Overview

The Professional Data Engineer certification exam is a 2-hour test consisting of the multiple-choice and multiple-select questions. The students can take it in the English or Japanese languages. To register for and schedule the exam, you must pay the fee of $200. It is possible to sit for this test in an online proctored format at a remote location. You can also take it as an on-site proctored exam at a designated testing center.

Ensure Solution Quality

• Design for Compliance & Security: The consideration for this topic includes identity & access management such as Cloud IAM. You should also know about data security (including key management and encryption) and privacy assurance (such as Data Loss Prevention API). This part also covers the skills needed in legal compliance, including Health Insurance Portability & Accountability Act, FedRAMP, Children’s Online Privacy Protection Act, and General Data Protection Regulation;
• Ensure Portability & Flexibility: The considerations for this domain include the design for application and data portability, including data residency prerequisites and Multiple-Cloud. It also coves data staging, discovery, and cataloging, as well as mapping to future and current business prerequisites.
• Ensure Efficiency & Scalability: The potential candidates will be required to demonstrate their ability to build and run test suits as well as monitor pipeline, including Stackdriver. It also focuses on their skills related to assessing, improving, and troubleshooting data process infrastructure and data representations. This area will also require that the test takers demonstrate the capacity to resize and autoscale resources;
• Ensure Fidelity & Reliability: The applicants should be able to carry out data preparation & quality control (such as Cloud Dataprep), verify and monitor, as well as plan, execute, and stress test data recovery (including rerunning failed jobs, fault tolerance, and retrospective re-analysis performance). Besides that, they should be able to choose between idempotent ACID and eventual consistent prerequisites;

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