Google Certified Professional Data Engineer : Professional-Data-Engineer

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Google Professional Data Engineer Certified Professional salary

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

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

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

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Operationalize ML Models

• Select the Relevant Training & Service Infrastructure: The consideration for this topic includes distributed versus single machine, hardware accelerators (such as TPU and GPU), and edge compute usage;
• Deploy Machine Learning Pipelines: This objective requires your competence in ingesting relevant data, continuous evaluation, and retraining of ML models (Kuberflow, BigQuery Machine Learning, Cloud Machine Learning Engine, and Spark Machine Learning);
• Measure, Troubleshoot & Monitor Machine Learning Models: The focus of this subtopic includes the effect of dependencies on machine learning models. It will also measure the examinees’ understanding of machine learning terminologies, such as features, regression, labels, classification, models, recommendation, evaluation metrics, and unsupervised & supervised learning. Moreover, it will also assess their knowledge of common sources of error such as assumptions regarding data.
• Leverage Pre-Built Machine Learning Models as a Service: It covers one’s knowledge and skills in customizing machine learning APIs, including Auto ML text and Auto ML Vision. It also covers the conversational experiences, such as Dialogflow as well as machine learning APIs, including Speech API and Vision API;

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

The following will be discussed here:

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

Google Professional-Data-Engineer Exam Syllabus Topics: