PDF Version DemoUnderstanding functional and technical aspects of Google Professional Data Engineer Exam Designing data processing systems
The following will be discussed here:
- Job automation and orchestration (e.g., Cloud Composer)
- Online (interactive) vs. batch predictions
- System availability and fault tolerance
- Hybrid cloud and edge computing
- Use of distributed systems
- Choice of infrastructure
- Data modeling
- Data publishing and visualization (e.g., BigQuery)
- At least once, in-order, and exactly once, etc., event processing
- Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)
- Selecting the appropriate storage technologies
- Distributed systems
- Designing data pipelines
- Designing data processing systems
- Capacity planning
- Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka)
- Schema design
- Mapping storage systems to business requirements
- Tradeoffs involving latency, throughput, transactions
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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Professional Data Engineer Exam Details
Like other Google exams, this exam also consists of multiple choice and multiple select questions. Consider the fact that you need to pay $200 for the registration. After that, you will access the test for 2 hours which is presented either in English or Japanese. Moreover, you can either take the exam online or have to find a test center near your place to take this test.
There is no formal prerequisite for the exam but it is recommended to have 3-4 years of experience within the data engineering field and to be responsible for the tasks related to data engineering and machine learning. So, on the final test day, you need to have exhaustive knowledge about these domains to perform your best.
- Building data processing systems
- Providing solution quality
- Designing data processing systems
- Operationalizing machine learning models
Google Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Ensuring solution quality and reliability | 17% | - Testing and validating data systems
|
| Topic 2: Designing data processing systems | 20% | - Designing for regulatory and security requirements
|
| Topic 3: Maintaining and automating data workloads | 18% | - Resource optimization
|
| Topic 4: Building and operationalizing data processing systems | 25% | - Deploying and managing systems
|
| Topic 5: Operationalizing machine learning models | 20% | - Preparing data for ML
|
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