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Don't wait and enroll in these training courses offered by the official vendor that will help you ace the Professional Data Engineer exam with a good score. Once you take this exam and earn the prestigious Professional Data Engineer certification, you will get a chance to obtain a high-paying job and an amazing opportunity to work with the experts. Don't waste your time on other tasks and start preparing for this exam today. The more you practice the more you will get closer to success as a data analyst or data engineer. Moreover, it will polish your skills throughout and allow you to efficiently in well-reputed companies.

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Who is the Professional Data Engineer Exam Intended for?

This exam is designed for individuals who are experts in designing, building, securing, and monitoring data processing systems with a particular emphasis on compliance and security. The candidate who wants to take the Professional Data Engineer exam should have the ability to deploy, leverage, and training pre-existing machine learning models. Moreover, every applicant should have experience of more than 3 years including 1-year experience in designing and handling solutions utilizing GCP.

Exam Topics

The syllabus of the Google Professional Data Engineer exam is divided into 4 topics, each covering specific knowledge and skills that the candidates need to develop while preparing for the test. A full outline of the exam content can be viewed on the official website. The highlights of the domains covered in the test are as follows:

Topic 1. Designing Data Processing Systems

To answer the questions related to this first topic of the certification exam, the individuals need to demonstrate their proficiency in selecting the proper storage technologies. This includes their understanding of data modeling, schema design, distributed systems, as well as tradeoffs involving throughput, latency, and transactions. Moreover, the applicants need to have the ability to map storage systems to the business needs. It also measures one’s skills in designing data pipelines, designing a data processing solution, as well as migrating data warehousing & data processing.

Understanding functional and technical aspects of Google Professional Data Engineer Exam Ensuring solution quality

The following will be discussed here:

• Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))
• Data staging, cataloging, and discovery
• Ensuring privacy (e.g., Data Loss Prevention API)
• Identity and access management (e.g.,Cloud IAM)
• Ensuring scalability and efficiency
• Designing for data and application portability (e.g., multi-cloud, data residency requirements)
• Ensuring flexibility and portability
• Ensuring reliability and fidelity
• Designing for security and compliance
• Performing data preparation and quality control (e.g., Cloud Dataprep)
• Data security (encryption, key management)
• Choosing between ACID, idempotent, eventually consistent requirements
• Assessing, troubleshooting, and improving data representations and data processing infrastructure
• Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
• Mapping to current and future business requirements
• Building and running test suites
• Pipeline monitoring (e.g., Stackdriver)
• Resizing and autoscaling resources
• Verification and monitoring

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

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