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Preparation Process

To perform well in the Google Professional Data Engineer certification exam, the candidates must be ready to devote ample time to preparation. There is a host of study materials available on the Internet, but if you want to be confident in the authenticity of the resources that you use, it is best to refer to the official platform. Google recommends that the applicants follow the Professional Data Engineer learning path, which is a comprehensive option involving in-person classes, online training, hands-on labs, and other resources from Google Cloud.

Besides that, it is recommended that the students use the official sample questions to familiarize themselves with the question formats that they will encounter during the actual exam. The official webpage also contains additional resources such as Google Cloud documentation and Google Cloud solutions. There is also an option of joining the subject-related webinar to get valuable preparation tips from the Google experts.

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

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

The following will be discussed here:

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

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Data Engineering on Google Cloud course

It is a 4-day course that gives hands-on experience to the candidates and allows them to build data processing systems on Google Cloud. It will also show you how to design data processing systems, analyze data and build end-to-end data pipelines and machine learning. In order to get a better understanding of the course, you need to complete the big data machine learning course or get equivalent experience. This course also aids you in developing applications using a programming language such as Python and covers the following objective:

• Predicting machine models using TensorFlow and Cloud ML
• Designing and building data processing systems on the Google Cloud Platform
• Influencing unstructured data using ML APIs on Cloud Dataproc
• Processing batch and streaming data by using autoscaling data pipelines on Cloud Dataflow
• Enable insights from streaming data