Google Professional-Data-Engineer : Google Certified Professional Data Engineer Exam

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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:

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

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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:

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

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

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The Professional Data Engineer exam is the industry-standard exam that proves the candidate’s ability to do data-driven decision-making by assembling, transforming, and publishing data. If you are rooting for a career in data engineering, you should take this test. It will lead you to attain the Professional Data Engineer certification issued by Google.

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