Difficulty in Attempting Google Professional Data Engineer Exam Certification

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

The following will be discussed here:

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

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

The following will be discussed here:

• Ensuring reliability and fidelity
• Building and running test suites
• Identity and access management (e.g.,Cloud IAM)
• Ensuring scalability and efficiency
• Pipeline monitoring (e.g., Stackdriver)
• Choosing between ACID, idempotent, eventually consistent requirements
• Data staging, cataloging, and discovery
• Ensuring flexibility and portability
• 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)
• Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
• Designing for security and compliance
• Ensuring privacy (e.g., Data Loss Prevention API)
• Verification and monitoring
• Mapping to current and future business requirements
• Assessing, troubleshooting, and improving data representations and data processing infrastructure
• Data security (encryption, key management)
• Resizing and autoscaling resources
• Designing for data and application portability (e.g., multi-cloud, data residency requirements)

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

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