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Google Professional-Data-Engineer certification exam is a tough test that measures the competency of candidates in a variety of areas related to data engineering. Professional-Data-Engineer exam consists of multiple-choice questions that cover topics such as data processing, data storage, data analysis, data visualization, and machine learning. Professional-Data-Engineer exam also includes hands-on tasks that require candidates to demonstrate their ability to use Google Cloud Platform tools and services to solve real-world data engineering problems.

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Google Professional-Data-Engineer Certification Exam is a highly recognized certification exam for professionals who want to validate their skills and expertise in data engineering. Professional-Data-Engineer exam is designed to test the candidates' knowledge of Google Cloud Platform (GCP) and its data engineering services. It requires a deep understanding of various data tooling, technologies, and services used to build and maintain data pipelines, data processing systems, and machine learning models.

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

Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Maintaining and automating data workloads18%- Automation and repeatability
  • 1. Automating deployment and updates
  • 2. Implementing CI/CD for data systems
- Resource optimization
  • 1. Choosing appropriate compute and storage options
  • 2. Cost management and resource allocation
Topic 2: Operationalizing machine learning models20%- Deploying and maintaining ML models
  • 1. Model serving and monitoring
  • 2. Optimizing model performance and cost
- Preparing data for ML
  • 1. Handling structured and unstructured data
  • 2. Feature engineering and data preparation
Topic 3: Building and operationalizing data processing systems25%- Building data pipelines
  • 1. Ingesting data from various sources
  • 2. Transforming and cleaning data
  • 3. Orchestrating data workflows
- Deploying and managing systems
  • 1. Monitoring and logging data processes
  • 2. Managing infrastructure and resources
Topic 4: Designing data processing systems20%- Designing for regulatory and security requirements
  • 1. Implementing access control and data protection
  • 2. Ensuring data privacy and compliance
- Designing for business requirements
  • 1. Designing for reliability and fault tolerance
  • 2. Selecting appropriate storage solutions
  • 3. Designing for scalability and elasticity
Topic 5: Ensuring solution quality and reliability17%- Testing and validating data systems
  • 1. Data quality validation
  • 2. Performance and scalability testing
- Troubleshooting and optimization
  • 1. Diagnosing performance issues
  • 2. Optimizing queries and workloads
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