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The benefit of obtaining the Google Professional Data Engineer Exam Certification

A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A data engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A data engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.

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:

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

Operationalizing Machine Learning Models

Here the candidates need to demonstrate their expertise in using pre-built Machine Learning models as a service, including Machine Learning APIs (for instance, Speech API, Vision API, etc.), customizing Machine Learning APIs (for instance, Auto ML text, AutoML Vision, etc.), conversational experiences (for instance, Dialogflow). The applicants should also have the skills in deploying the Machine Learning pipeline. This involves the ability to ingest relevant data, perform retraining of machine learning models (BigQuery ML, Cloud Machine Learning Engine, Spark ML, Kubeflow), as well as execute continuous evaluation. Additionally, the students should be able to choose the relevant training & serving infrastructure as well as know how to fulfill measuring, monitoring, and troubleshooting of Machine Learning models.

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

The following will be discussed here:

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

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