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Google Professional-Data-Engineer exam is designed to test an individual's ability to design, build, and maintain data processing systems on Google Cloud Platform. Professional-Data-Engineer exam is intended for data engineers, developers, and other IT professionals who are responsible for designing and implementing data solutions on Google Cloud Platform. Professional-Data-Engineer exam covers a broad range of topics, including data processing, data warehousing, data analysis, and machine learning.

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

The following will be discussed here:

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

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To be eligible for the Google Professional-Data-Engineer exam, candidates are required to have a deep understanding of data processing technologies, such as Hadoop, Spark, and other big data frameworks. They should also be proficient in programming languages such as Python, Java, or Go, and have experience in designing and developing data processing pipelines. Additionally, candidates should have hands-on experience working with Google Cloud Platform services such as BigQuery, Dataflow, and Dataproc. Passing the Google Professional-Data-Engineer exam can prove to be a valuable asset for data professionals who want to advance their careers or demonstrate their expertise in managing data solutions on Google Cloud.

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

Google Professional-Data-Engineer Exam Syllabus Topics: