Ensure Solution Quality

• Ensure Fidelity & Reliability: The applicants should be able to carry out data preparation & quality control (such as Cloud Dataprep), verify and monitor, as well as plan, execute, and stress test data recovery (including rerunning failed jobs, fault tolerance, and retrospective re-analysis performance). Besides that, they should be able to choose between idempotent ACID and eventual consistent prerequisites;
• Design for Compliance & Security: The consideration for this topic includes identity & access management such as Cloud IAM. You should also know about data security (including key management and encryption) and privacy assurance (such as Data Loss Prevention API). This part also covers the skills needed in legal compliance, including Health Insurance Portability & Accountability Act, FedRAMP, Children’s Online Privacy Protection Act, and General Data Protection Regulation;
• Ensure Portability & Flexibility: The considerations for this domain include the design for application and data portability, including data residency prerequisites and Multiple-Cloud. It also coves data staging, discovery, and cataloging, as well as mapping to future and current business prerequisites.
• Ensure Efficiency & Scalability: The potential candidates will be required to demonstrate their ability to build and run test suits as well as monitor pipeline, including Stackdriver. It also focuses on their skills related to assessing, improving, and troubleshooting data process infrastructure and data representations. This area will also require that the test takers demonstrate the capacity to resize and autoscale resources;

Understanding functional and technical aspects of Google Professional Data Engineer Exam Operationalizing machine learning models

The following will be discussed here:

• Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
• Measuring, monitoring, and troubleshooting machine learning models
• Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
• Hardware accelerators (e.g., GPU, TPU)
• Conversational experiences (e.g., Dialogflow)
• Ingesting appropriate data
• Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
• ML APIs (e.g., Vision API, Speech API)
• Distributed vs. single machine
• Operationalizing machine learning models
• Use of edge compute
• Deploying an ML pipeline
• Common sources of error (e.g., assumptions about data)
• Impact of dependencies of machine learning models
• Leveraging pre-built ML models as a service
• Choosing the appropriate training and serving infrastructure
• Continuous evaluation

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

Google Professional Data Engineer Certified Professional salary

The average salary of a Google Professional Data Engineer Certified Expert in

• England - 115,632 POUND
• India - 25,42,327 INR
• Europe - 135,347 EURO
• United State - 151,247 USD