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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Understanding functional and technical aspects of Google Professional Data Engineer Exam Designing data processing systems

The following will be discussed here:

• Mapping storage systems to business requirements
• Distributed systems
• Designing data pipelines
• Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)
• Designing data processing systems
• Online (interactive) vs. batch predictions
• Data modeling
• Tradeoffs involving latency, throughput, transactions
• Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka)
• Selecting the appropriate storage technologies
• Job automation and orchestration (e.g., Cloud Composer)
• Choice of infrastructure
• Schema design
• Use of distributed systems
• Capacity planning
• Data publishing and visualization (e.g., BigQuery)
• At least once, in-order, and exactly once, etc., event processing
• Hybrid cloud and edge computing
• System availability and fault tolerance

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

The following will be discussed here:

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