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

The following will be discussed here:

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

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

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What is the duration, language, and format of Google Professional Data Engineer Exam

• Length of Examination: 120 minutes
• Passing score: 80%
• Language: English (U.S.), Japanese, Spanish, and Portuguese
• Number of Questions: 50-60
• Format: Multiple choices, multiple answers
• Cost: $200

The Google Professional Data Engineer certification is designed to equip the individuals with the required knowledge and skills to enable data-driven decision-making through collecting, transforming, and publishing data. To earn this certificate, the candidates will be required to pass a single test measuring their skills in leveraging, deploying, and continuously training pre-existing machine learning models. The qualifying exam also evaluates the ability of the applicants to design, build, operationalize, monitor, and secure data processing systems.