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Building & Operationalizing Data Processing Systems
Within this subject area, the test takers should show that they know how to build and operationalize storage systems. Specifically, they need to be conversant with effective use of managed services (such as Cloud Bigtable, Cloud SQL, Cloud Spanner, BigQuery, Cloud Storage, Cloud Memorystore, Cloud Datastore), storage costs & performance, and lifecycle management of data. The students should also be capable of building as well as operationalizing pipelines, including such technical tasks as data cleansing, transformation, batch & streaming, data acquisition & import, and integrating with new data sources. Apart from that, the candidates need to have sufficient competency to build and operationalize the processing infrastructure. This includes a good comprehension of provisioning resources, adjusting pipelines, monitoring pipelines, as well as testing & quality control.
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.
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The benefit of obtaining the Google Professional Data Engineer Exam Certification
A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A data engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A data engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.
Reference: https://cloud.google.com/certification/data-engineer
How to book Google Professional Data Engineer Exams
The registration for the Google Professional Data Engineer Exam follows the steps given below.
- Step4: Take the date of the exam, choose exam center and make further payment using payment method like credit/debit etc.
- Step1: Visit the Google Cloud Webassessor Website
- Step3: Search for the exam name Google Professional Data Engineer
- Step2: Sign in or sign up to your Google Cloud Webassessor account
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Google Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Operationalizing machine learning models | 20% | - Deploying and maintaining ML models
|
| Topic 2: Maintaining and automating data workloads | 18% | - Automation and repeatability
|
| Topic 3: Designing data processing systems | 20% | - Designing for regulatory and security requirements
|
| Topic 4: Ensuring solution quality and reliability | 17% | - Testing and validating data systems
|
| Topic 5: Building and operationalizing data processing systems | 25% | - Building data pipelines
|
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