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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:

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

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

Who should take the Google Professional Data Engineer exam

Individuals should pursue the exam if they want to demonstrate their expertise and ability to design and develop Data Engineering. Following professional get benefited from Google Professional Data Engineer Certification

• Business analysts
• Data engineers
• Developers responsible for managing big data transformation initiatives
• Data analysts
• Data architects
• Data scientists

Difficulty in Attempting Google Professional Data Engineer Exam Certification

If the user has successfully passed the professional-data-engineer practice exam and has been through professional-data-engineer exam dumps then the certification exam will not be too much difficult as the user has shown aptitude for understanding complicated processes.