We're so confident of our products that we provide no hassle product exchange.
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.
Understanding functional and technical aspects of Google Professional Data Engineer Exam Operationalizing machine learning models
The following will be discussed here:
- Use of edge compute
- Operationalizing machine learning models
- Common sources of error (e.g., assumptions about data)
- Choosing the appropriate training and serving infrastructure
- Leveraging pre-built ML models as a service
- Hardware accelerators (e.g., GPU, TPU)
- Impact of dependencies of machine learning models
- Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
- Continuous evaluation
- Ingesting appropriate data
- Conversational experiences (e.g., Dialogflow)
- Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
- Deploying an ML pipeline
- Distributed vs. single machine
- 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)
Reference: https://cloud.google.com/certification/data-engineer
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.
This course is normally taken by data scientists, data analysts, and business analysts who are in the field of Google Сloud. It is a good way to prepare for your final exam because it teaches you all the details including 7 modules that cover all the Professional Data Engineer exam objectives:
- Compute and Storage Fundamentals
- Scaling Data Analytics
- Introducing Google Cloud Platform ‘
- Data Processing Architectures
- Machine Learning
- Data Analytics on the Cloud
- Additional Resources
By Addison
