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

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

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

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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.

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

Google Professional-Data-Engineer Exam Syllabus Topics: