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To be eligible for the Google Professional-Data-Engineer certification, candidates need to have a strong understanding of data engineering principles and practical experience in building and deploying data processing systems. Google Certified Professional Data Engineer Exam certification is ideal for professionals who work in data engineering or related fields, such as data analysis, business intelligence, and data science. It is also suitable for individuals who aspire to become data engineers and want to learn more about the field.

Reference: https://cloud.google.com/certification/data-engineer

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Google Professional-Data-Engineer certification is a highly respected and in-demand certification for data professionals. Google Certified Professional Data Engineer Exam certification is designed for individuals who possess the knowledge and skills to design, build, maintain, and troubleshoot data processing systems with a particular emphasis on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is offered by Google and is recognized globally as a valuable credential for professionals in the data engineering field.

The Google Professional-Data-Engineer exam tests a candidate's proficiency in various aspects of data engineering such as designing data processing systems, building and operationalizing systems, optimizing and securing data networks, and developing high-quality machine learning models. Professional-Data-Engineer exam's format includes multiple-choice questions, case studies, and hands-on scenarios that test a candidate's practical knowledge of working with Google Cloud technologies.

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Google Professional-Data-Engineer certification is a popular certification for professionals in the field of data engineering. Google Certified Professional Data Engineer Exam certification validates the knowledge and skills of professionals in designing and building data processing systems, as well as their ability to analyze and use machine learning models. Google Certified Professional Data Engineer Exam certification is gaining popularity among professionals who want to validate their skills and knowledge in this field and advance their careers.

Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Ingesting and processing the data (~20% of the exam)20%- Deploying and operationalizing the pipelines
  • 1. CI/CD for data pipelines
  • 2. Job automation and orchestration (Cloud Composer, Workflows)
- Building and maintaining data structures and databases
  • 1. Defining data lifecycle
  • 2. Planning for analytical and operational use cases
- Performing security considerations
  • 1. Data encryption
  • 2. Auditing, privacy, and compliance
  • 3. Identity and Access Management (IAM)
Storing the data (~20% of the exam)20%- Selecting storage systems
  • 1. Analyzing data access patterns
  • 2. Lifecycle management of data
  • 3. Planning for storage costs and performance
- Planning for using a data warehouse
  • 1. Defining architecture to support data access patterns
  • 2. Mapping business requirements
  • 3. Deciding the degree of data normalization
  • 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
- Using a data lake
  • 1. Monitoring the data lake
  • 2. Processing data
  • 3. Managing the lake (data discovery, access, cost controls)
Maintaining and automating data workloads (~15% of the exam)15%- Designing for reliability and fidelity
  • 1. Recovering from failures
  • 2. Performing data quality and validation checks
  • 3. Planning for monitoring and alerting
- Automating data processes
  • 1. Scheduling jobs
  • 2. Workflow orchestration
  • 3. Continuous integration and continuous deployment (CI/CD)
- Monitoring data pipelines and data processes
  • 1. Logging, monitoring, and troubleshooting
  • 2. Managing quotas and resource usage
Preparing and using data for analysis (~15% of the exam)15%- Preparing data for visualization
  • 1. Connecting to Looker and other BI tools
  • 2. Preparing data for reporting and dashboards
- Sharing data securely
  • 1. Data sharing and collaboration
  • 2. Publishing datasets
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 processing resources
  • 1. Cost optimization
  • 2. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
  • 3. Cluster sizing and autoscaling
- Designing data pipelines
  • 1. AI data enrichment
  • 2. Integrating with new data sources
  • 3. Data acquisition and import
  • 4. Batch processing
  • 5. Processing logic
  • 6. Streaming (e.g., windowing, late arriving data)

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