Difficulty in Attempting Google Professional Data Engineer Exam Certification
If the user has successfully passed the professional-data-engineer practice exam and has been through professional-data-engineer exam dumps then the certification exam will not be too much difficult as the user has shown aptitude for understanding complicated processes.
Reference: https://cloud.google.com/certification/data-engineer
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.
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What is the duration, language, and format of Google Professional Data Engineer Exam
- Length of Examination: 120 minutes
- Language: English (U.S.), Japanese, Spanish, and Portuguese
- Number of Questions: 50-60
- Format: Multiple choices, multiple answers
- Passing score: 80%
- Cost: $200
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Google Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Maintaining and automating data workloads | 18% | - Automation and repeatability
|
| Building and operationalizing data processing systems | 25% | - Deploying and managing systems
|
| Operationalizing machine learning models | 20% | - Preparing data for ML
|
| Designing data processing systems | 20% | - Designing for regulatory and security requirements
|
| Ensuring solution quality and reliability | 17% | - Testing and validating data systems
|
