PDF Version DemoHow to Prepare For Google Professional Data Engineer Exam
Preparation Guide for Google Professional Data Engineer Exam
Introduction to Google Professional Data Engineer Exam
Google has established a path for IT professionals endorse as a Data Engineer on the GCP platform. This accreditation program gives Google cloud professionals a way to endorse their skills. The evaluation relies on a meticulous exam using industry standard methodology to conclude whether or not a aspirant meets Google's proficiency standards.
The Professional Data Engineer exam assesses your ability to:
- Design data processing systems
- Operationalize machine learning models
- Build and operationalize data processing systems
- Ensure solution quality
Google Professional Data Engineer Exam certification is evidence of your skills, expertise in those areas in which you like to work. If candidate wants to work on Google Professional Data Engineer and prove his knowledge, Certification offered by Google. This Google Professional Data Engineer Certification helps a candidate to validates his skills in Big Data and Data engineering Technology.
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.
Reference: https://cloud.google.com/certification/data-engineer
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Google Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Building and operationalizing data processing systems | 24% | - Data ingestion and integration
|
| Topic 2: Designing data processing systems | 22% | - Data architecture and storage design
|
| Topic 3: Ensuring solution quality | 28% | - Reliability and performance
|
| Topic 4: Operationalizing machine learning models | 26% | - ML pipeline integration
|
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