PDF VersionThis 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:
- Additional Resources
- Compute and Storage Fundamentals
- Introducing Google Cloud Platform ‘
- Data Analytics on the Cloud
- Machine Learning
- Data Processing Architectures
- Scaling Data Analytics
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Certification Path
The Google Professional Data Engineer Certification is one of the highest level of certification mainly focussing to the professional Data Engineering.
There is no prerequisite for this exam but still it would be best to follow some sequence in order to prove immense knowledge as a Google professional Data Engineer.
You can complete Google Associate Certifications then approach for the professional certification. For more information related to Google cloud certification track Google-certification-path
Operationalizing Machine Learning Models
Here the candidates need to demonstrate their expertise in using pre-built Machine Learning models as a service, including Machine Learning APIs (for instance, Speech API, Vision API, etc.), customizing Machine Learning APIs (for instance, Auto ML text, AutoML Vision, etc.), conversational experiences (for instance, Dialogflow). The applicants should also have the skills in deploying the Machine Learning pipeline. This involves the ability to ingest relevant data, perform retraining of machine learning models (BigQuery ML, Cloud Machine Learning Engine, Spark ML, Kubeflow), as well as execute continuous evaluation. Additionally, the students should be able to choose the relevant training & serving infrastructure as well as know how to fulfill measuring, monitoring, and troubleshooting of Machine Learning models.
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:
- Enable insights from streaming data
- Designing and building data processing systems on the Google Cloud Platform
- Processing batch and streaming data by using autoscaling data pipelines on Cloud Dataflow
- Influencing unstructured data using ML APIs on Cloud Dataproc
- Predicting machine models using TensorFlow and Cloud ML
Reference: https://cloud.google.com/certification/data-engineer
Google Professional-Data-Engineer日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Operationalizing machine learning models | 20% | - Preparing data for ML
|
| Ensuring solution quality and reliability | 17% | - Troubleshooting and optimization
|
| Maintaining and automating data workloads | 18% | - Resource optimization
|
| Building and operationalizing data processing systems | 25% | - Building data pipelines
|
| Designing data processing systems | 20% | - Designing for business requirements
|
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