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How to Prepare For Google Professional Data Engineer Exam

Preparation Guide for Google Professional Data Engineer Exam

Introduction to Google Professional Data Engineer Exam

Google has established a path for IT professionals endorse as a Data Engineer on the GCP platform. This accreditation program gives Google cloud professionals a way to endorse their skills. The evaluation relies on a meticulous exam using industry standard methodology to conclude whether or not a aspirant meets Google's proficiency standards.

The Professional Data Engineer exam assesses your ability to:

• Build and operationalize data processing systems
• Design data processing systems
• Ensure solution quality
• Operationalize machine learning models

Google Professional Data Engineer Exam certification is evidence of your skills, expertise in those areas in which you like to work. If candidate wants to work on Google Professional Data Engineer and prove his knowledge, Certification offered by Google. This Google Professional Data Engineer Certification helps a candidate to validates his skills in Big Data and Data engineering Technology.

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:

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

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Who is the Professional Data Engineer Exam Intended for?

This exam is designed for individuals who are experts in designing, building, securing, and monitoring data processing systems with a particular emphasis on compliance and security. The candidate who wants to take the Professional Data Engineer exam should have the ability to deploy, leverage, and training pre-existing machine learning models. Moreover, every applicant should have experience of more than 3 years including 1-year experience in designing and handling solutions utilizing GCP.

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