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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)
- Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
- Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
- Ingesting appropriate data
- Impact of dependencies of machine learning models
- Measuring, monitoring, and troubleshooting machine learning models
- Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
- Choosing the appropriate training and serving infrastructure
- Leveraging pre-built ML models as a service
- Continuous evaluation
- Deploying an ML pipeline
- Conversational experiences (e.g., Dialogflow)
- Use of edge compute
- Common sources of error (e.g., assumptions about data)
- ML APIs (e.g., Vision API, Speech API)
- Operationalizing machine learning models
- Distributed vs. single machine
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Exam Overview
The Professional Data Engineer certification exam is a 2-hour test consisting of the multiple-choice and multiple-select questions. The students can take it in the English or Japanese languages. To register for and schedule the exam, you must pay the fee of $200. It is possible to sit for this test in an online proctored format at a remote location. You can also take it as an on-site proctored exam at a designated testing center.
Reference: https://cloud.google.com/certification/data-engineer
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Career Opportunities
The certified individuals can explore a variety of job opportunities. Some of the positions that they can take up include a Software Engineer, a Cloud Architect, a Data Engineer, a Sales Engineer, a Data Scientist, a Cloud Developer, and a Kubernetes Architect, among others. The salary outlook for these job roles is an average of $128,500 per annum.
Google Professional-Data-Engineer日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Ensuring solution quality | 28% | - Security and governance
|
| Designing data processing systems | 22% | - Data architecture and storage design
|
| Operationalizing machine learning models | 26% | - Model deployment and monitoring
|
| Building and operationalizing data processing systems | 24% | - Data processing and transformation
|



