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Difficulty in Attempting Google Professional Data Engineer Exam Certification
If the user has successfully passed the professional-data-engineer practice exam and has been through professional-data-engineer exam dumps then the certification exam will not be too much difficult as the user has shown aptitude for understanding complicated processes.
Reference: https://cloud.google.com/certification/data-engineer
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:
- Influencing unstructured data using ML APIs on Cloud Dataproc
- Processing batch and streaming data by using autoscaling data pipelines on Cloud Dataflow
- Designing and building data processing systems on the Google Cloud Platform
- Predicting machine models using TensorFlow and Cloud ML
- Enable insights from streaming data
Understanding functional and technical aspects of Google Professional Data Engineer Exam Operationalizing machine learning models
The following will be discussed here:
- Continuous evaluation
- Measuring, monitoring, and troubleshooting machine learning models
- ML APIs (e.g., Vision API, Speech API)
- Conversational experiences (e.g., Dialogflow)
- Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
- Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
- Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
- Distributed vs. single machine
- Use of edge compute
- Common sources of error (e.g., assumptions about data)
- Impact of dependencies of machine learning models
- Hardware accelerators (e.g., GPU, TPU)
- Ingesting appropriate data
- Leveraging pre-built ML models as a service
- Operationalizing machine learning models
- Deploying an ML pipeline
- Choosing the appropriate training and serving infrastructure
Target Audience
The candidates for this certification are the data engineers or those aiming to become one. These individuals should have the capacity to allow data-driven decision-making through the collection, transformation, and publishing of data. They have the expertise in designing, building, and operationalizing secure data processing systems and monitoring the same. This is with the specific emphasis on compliance and security, fidelity and reliability, portability and flexibility, as well as efficiency and scalability.
By Verna
