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

The following will be discussed here:

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

Conclusion

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

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

Bearable cost

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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.

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