Google Professional-Data-Engineer Q&A - in .pdf

  • Exam Code: Professional-Data-Engineer
  • Exam Name: Google Certified Professional Data Engineer Exam
  • Q & A: 433 Questions and Answers
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Google Professional-Data-Engineer Q&A - Testing Engine

  • Exam Code: Professional-Data-Engineer
  • Exam Name: Google Certified Professional Data Engineer Exam
  • Q & A: 433 Questions and Answers
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By obtaining the Google Professional-Data-Engineer certification, professionals can demonstrate their ability to design, build, and maintain efficient data processing systems on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification not only enhances their professional credentials but also opens up new career opportunities in the field of data engineering. With the rising demand for data engineers, this certification can prove to be a valuable asset for professionals looking to advance their careers in this field.

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Google Professional-Data-Engineer exam is a certification exam offered by Google Cloud Platform. It is designed for data professionals who are looking to demonstrate their expertise in designing, building, and managing data processing systems on Google Cloud Platform. Professional-Data-Engineer exam tests the candidate's knowledge of data engineering, data processing, and data analysis techniques using Google Cloud Platform tools and services.

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

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