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Certification Path

The Google Professional Data Engineer Certification is one of the highest level of certification mainly focussing to the professional Data Engineering.

There is no prerequisite for this exam but still it would be best to follow some sequence in order to prove immense knowledge as a Google professional Data Engineer.

You can complete Google Associate Certifications then approach for the professional certification. For more information related to Google cloud certification track Google-certification-path

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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:

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

For more info visit:

Google-provided tutorials Community-provided tutorials Google-Data-Engineer-Practice-Test

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

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