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

The following will be discussed here:

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

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Google Professional-Data-Engineer日本語 Exam Syllabus Topics:

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

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

The following will be discussed here:

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

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Who should take the Google Professional Data Engineer exam

Individuals should pursue the exam if they want to demonstrate their expertise and ability to design and develop Data Engineering. Following professional get benefited from Google Professional Data Engineer Certification

• Business analysts
• Developers responsible for managing big data transformation initiatives
• Data engineers
• Data architects
• Data scientists
• Data analysts

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