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

The following will be discussed here:

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

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

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

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.

Preparation Process

To perform well in the Google Professional Data Engineer certification exam, the candidates must be ready to devote ample time to preparation. There is a host of study materials available on the Internet, but if you want to be confident in the authenticity of the resources that you use, it is best to refer to the official platform. Google recommends that the applicants follow the Professional Data Engineer learning path, which is a comprehensive option involving in-person classes, online training, hands-on labs, and other resources from Google Cloud.

Besides that, it is recommended that the students use the official sample questions to familiarize themselves with the question formats that they will encounter during the actual exam. The official webpage also contains additional resources such as Google Cloud documentation and Google Cloud solutions. There is also an option of joining the subject-related webinar to get valuable preparation tips from the Google experts.