Requirements

The certification does not have any official prerequisites. However, it is advised to have at least three years of industry experience with one or more years of expertise in designing and managing different solutions with the use of Google Cloud Platform. It is also required to review the topics of the qualifying exam before sitting for it.

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

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Ensuring Solution Quality

The last section of the certification exam evaluates the ability of the learners to design for security & compliance, including identity & access management, legal compliance, data security, and privacy ensuring. Moreover, they should be able to ensure flexibility & portability, reliability & fidelity, as well as scalability & efficiency.

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