Exam Name: Google Certified Professional Data Engineer Exam
Certification Provider: Google
Corresponding Certification: Google Cloud Certified
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- Total Questions: 403
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Prerequisites
There are no formal requirements that the candidates need to meet to qualify for the Google Professional Data Engineer certification. However, without some level of professional experience, it will be difficult for the students to ace the qualifying test. The target individuals are recommended to have three or more years of industry experience, including one or more years of experience in designing and managing solutions with the help of Google Cloud Platform. It is preferable that the applicants also possess some basic database knowledge.
For more info visit:
Google-provided tutorials Community-provided tutorials Google-Data-Engineer-Practice-Test
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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:
- ML APIs (e.g., Vision API, Speech API)
- Choosing the appropriate training and serving infrastructure
- Measuring, monitoring, and troubleshooting machine learning models
- Impact of dependencies of machine learning models
- Operationalizing machine learning models
- Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
- Leveraging pre-built ML models as a service
- Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
- Use of edge compute
- Ingesting appropriate data
- Continuous evaluation
- Distributed vs. single machine
- Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
- Hardware accelerators (e.g., GPU, TPU)
- Common sources of error (e.g., assumptions about data)
- Conversational experiences (e.g., Dialogflow)
- Deploying an ML pipeline
Reference: https://cloud.google.com/certification/data-engineer
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