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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.

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

Target Audience

The candidates for this certification are the data engineers or those aiming to become one. These individuals should have the capacity to allow data-driven decision-making through the collection, transformation, and publishing of data. They have the expertise in designing, building, and operationalizing secure data processing systems and monitoring the same. This is with the specific emphasis on compliance and security, fidelity and reliability, portability and flexibility, as well as efficiency and scalability.

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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:

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

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