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The benefit of obtaining the Google Professional Data Engineer Exam Certification

A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A data engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A data engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.

How to book Google Professional Data Engineer Exams

The registration for the Google Professional Data Engineer Exam follows the steps given below.

• Step3: Search for the exam name Google Professional Data Engineer
• Step1: Visit the Google Cloud Webassessor Website
• Step2: Sign in or sign up to your Google Cloud Webassessor account
• Step4: Take the date of the exam, choose exam center and make further payment using payment method like credit/debit etc.

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:

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

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