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

The following will be discussed here:

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

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

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.

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

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.

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Data Engineering on Google Cloud course

It is a 4-day course that gives hands-on experience to the candidates and allows them to build data processing systems on Google Cloud. It will also show you how to design data processing systems, analyze data and build end-to-end data pipelines and machine learning. In order to get a better understanding of the course, you need to complete the big data machine learning course or get equivalent experience. This course also aids you in developing applications using a programming language such as Python and covers the following objective:

• Processing batch and streaming data by using autoscaling data pipelines on Cloud Dataflow
• Designing and building data processing systems on the Google Cloud Platform
• Enable insights from streaming data
• Influencing unstructured data using ML APIs on Cloud Dataproc
• Predicting machine models using TensorFlow and Cloud ML

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