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

The following will be discussed here:

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

Professional Data Engineer Exam Details

Like other Google exams, this exam also consists of multiple choice and multiple select questions. Consider the fact that you need to pay $200 for the registration. After that, you will access the test for 2 hours which is presented either in English or Japanese. Moreover, you can either take the exam online or have to find a test center near your place to take this test.

There is no formal prerequisite for the exam but it is recommended to have 3-4 years of experience within the data engineering field and to be responsible for the tasks related to data engineering and machine learning. So, on the final test day, you need to have exhaustive knowledge about these domains to perform your best.

• Building data processing systems
• Designing data processing systems
• Operationalizing machine learning models
• Providing solution quality

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Building & Operationalizing Data Processing Systems

Within this subject area, the test takers should show that they know how to build and operationalize storage systems. Specifically, they need to be conversant with effective use of managed services (such as Cloud Bigtable, Cloud SQL, Cloud Spanner, BigQuery, Cloud Storage, Cloud Memorystore, Cloud Datastore), storage costs & performance, and lifecycle management of data. The students should also be capable of building as well as operationalizing pipelines, including such technical tasks as data cleansing, transformation, batch & streaming, data acquisition & import, and integrating with new data sources. Apart from that, the candidates need to have sufficient competency to build and operationalize the processing infrastructure. This includes a good comprehension of provisioning resources, adjusting pipelines, monitoring pipelines, as well as testing & quality control.

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