Google Professional-Data-Engineer pass test : Google Certified Professional Data Engineer Exam

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Google Professional Data Engineer Practice Test Questions, Google Professional Data Engineer Exam Practice Test Questions

The Google Professional Data Engineer certification is designed to evaluate the candidates’ skills in designing data processing systems and ensuring solution quality. It is also created to measure their competence in building and operationalizing data processing systems and operationalizing ML models. The potential applicants must complete a single exam to get certified.

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:

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

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

The following will be discussed here:

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

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

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.

Google Professional-Data-Engineer Exam Syllabus Topics: