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Google Professional-Data-Engineer certification is a valuable asset for data professionals who are seeking to advance their career in the field of data engineering. It demonstrates that a candidate has the skills and knowledge required to design, build, and maintain data processing systems on Google Cloud Platform, which is a highly sought-after skill in today’s data-driven world.
To become a Google Certified Professional Data Engineer, a candidate must pass the certification exam, which costs $200. Professional-Data-Engineer exam is available in English, Japanese, and Spanish and can be taken online or at a testing center. Professional-Data-Engineer exam is valid for two years, after which a candidate must recertify to maintain their certification.
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To become a certified Google Professional Data Engineer, candidates must pass the Professional-Data-Engineer exam. Professional-Data-Engineer exam consists of multiple-choice and scenario-based questions that test the candidate's knowledge of Google Cloud data processing technologies, data storage solutions, and data analysis tools. Professional-Data-Engineer exam also evaluates the candidate's ability to design and implement data pipelines using Google Cloud services, such as BigQuery, Cloud Dataflow, and Cloud Dataproc.
Reference: https://cloud.google.com/certification/data-engineer
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Google Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
| Topic 1: Maintaining and automating data workloads (~15% of the exam) | 15% | - Monitoring data pipelines and data processes
- 1. Managing quotas and resource usage
- 2. Logging, monitoring, and troubleshooting
- Automating data processes
- 1. Scheduling jobs
- 2. Continuous integration and continuous deployment (CI/CD)
- 3. Workflow orchestration
- Designing for reliability and fidelity
- 1. Performing data quality and validation checks
- 2. Recovering from failures
- 3. Planning for monitoring and alerting
|
| Topic 2: Preparing and using data for analysis (~15% of the exam) | 15% | - Preparing data for visualization
- 1. Preparing data for reporting and dashboards
- 2. Connecting to Looker and other BI tools
- Sharing data securely
- 1. Publishing datasets
- 2. Data sharing and collaboration
|
| Topic 3: Designing data processing systems (~30% of the exam) | 30% | - Selecting appropriate storage technologies
- 1. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB
- 2. Mapping storage options to business requirements
- Designing data pipelines
- 1. AI data enrichment
- 2. Streaming (e.g., windowing, late arriving data)
- 3. Integrating with new data sources
- 4. Processing logic
- 5. Data acquisition and import
- 6. Batch processing
- Designing data processing resources
- 1. Cluster sizing and autoscaling
- 2. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
- 3. Cost optimization
|
| Topic 4: Ingesting and processing the data (~20% of the exam) | 20% | - Building and maintaining data structures and databases
- 1. Planning for analytical and operational use cases
- 2. Defining data lifecycle
- Performing security considerations
- 1. Auditing, privacy, and compliance
- 2. Identity and Access Management (IAM)
- 3. Data encryption
- Deploying and operationalizing the pipelines
- 1. Job automation and orchestration (Cloud Composer, Workflows)
- 2. CI/CD for data pipelines
|
| Topic 5: Storing the data (~20% of the exam) | 20% | - Using a data lake
- 1. Processing data
- 2. Managing the lake (data discovery, access, cost controls)
- 3. Monitoring the data lake
- Planning for using a data warehouse
- 1. Deciding the degree of data normalization
- 2. Defining architecture to support data access patterns
- 3. Mapping business requirements
- 4. Designing the data model
- Designing for a data platform
- 1. Building a federated governance model for distributed data systems
- 2. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage
- Selecting storage systems
- 1. Planning for storage costs and performance
- 2. Analyzing data access patterns
- 3. Lifecycle management of data
|