Difficulty in Attempting Google Professional Data Engineer Exam Certification
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Reference: https://cloud.google.com/certification/data-engineer
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Target Audience
The candidates for this certification are the data engineers or those aiming to become one. These individuals should have the capacity to allow data-driven decision-making through the collection, transformation, and publishing of data. They have the expertise in designing, building, and operationalizing secure data processing systems and monitoring the same. This is with the specific emphasis on compliance and security, fidelity and reliability, portability and flexibility, as well as efficiency and scalability.
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Understanding functional and technical aspects of Google Professional Data Engineer Exam Designing data processing systems
The following will be discussed here:
- Capacity planning
- Online (interactive) vs. batch predictions
- Data publishing and visualization (e.g., BigQuery)
- Job automation and orchestration (e.g., Cloud Composer)
- Tradeoffs involving latency, throughput, transactions
- System availability and fault tolerance
- Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)
- Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka)
- Data modeling
- Hybrid cloud and edge computing
- Selecting the appropriate storage technologies
- Designing data processing systems
- Designing data pipelines
- Mapping storage systems to business requirements
- Schema design
- At least once, in-order, and exactly once, etc., event processing
- Distributed systems
- Choice of infrastructure
- Use of distributed systems
Understanding functional and technical aspects of Google Professional Data Engineer Exam Ensuring solution quality
The following will be discussed here:
- Ensuring scalability and efficiency
- Performing data preparation and quality control (e.g., Cloud Dataprep)
- Data staging, cataloging, and discovery
- Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))
- Choosing between ACID, idempotent, eventually consistent requirements
- Assessing, troubleshooting, and improving data representations and data processing infrastructure
- Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
- Mapping to current and future business requirements
- Ensuring reliability and fidelity
- Designing for data and application portability (e.g., multi-cloud, data residency requirements)
- Resizing and autoscaling resources
- Pipeline monitoring (e.g., Stackdriver)
- Data security (encryption, key management)
- Ensuring privacy (e.g., Data Loss Prevention API)
- Verification and monitoring
- Identity and access management (e.g.,Cloud IAM)
- Ensuring flexibility and portability
- Building and running test suites
- Designing for security and compliance
Google Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Operationalizing machine learning models | 20% | - Deploying and maintaining ML models
|
| Designing data processing systems | 20% | - Designing for business requirements
|
| Maintaining and automating data workloads | 18% | - Automation and repeatability
|
| Building and operationalizing data processing systems | 25% | - Deploying and managing systems
|
| Ensuring solution quality and reliability | 17% | - Troubleshooting and optimization
|
