Google Certified Professional Data Engineer : Professional-Data-Engineer

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

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About Google Certified Professional Data Engineer : Professional-Data-Engineer Exam

What is the duration, language, and format of Google Professional Data Engineer Exam

  • Format: Multiple choices, multiple answers
  • Language: English (U.S.), Japanese, Spanish, and Portuguese
  • Number of Questions: 50-60
  • Passing score: 80%
  • Length of Examination: 120 minutes
  • Cost: $200

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This course is normally taken by data scientists, data analysts, and business analysts who are in the field of Google Сloud. It is a good way to prepare for your final exam because it teaches you all the details including 7 modules that cover all the Professional Data Engineer exam objectives:

  • Data Analytics on the Cloud
  • Scaling Data Analytics
  • Additional Resources
  • Introducing Google Cloud Platform ‘
  • Machine Learning
  • Compute and Storage Fundamentals
  • Data Processing Architectures

The candidates must develop practical skills in the exam topics to succeed. These objectives are highlighted below:

Design Data Processing Systems

  • Migrate Data Processing & Data Warehousing: This section includes validating migrations, migration from on-premises to Cloud, and awareness of the current state & how to migrate designs to the future state.
  • Design Data Processing Solutions: This topic includes the individuals’ expertise in planning, distributed systems usage, choice of infrastructure, hybrid Cloud & edge computing, system availability & fault tolerance. You should also know about the architecture options, including message queues, message brokers, service-oriented architecture, middleware, and serverless function;
  • Design Data Pipeline: The focus for this subsection includes data visualization & publishing and batch & streaming data (Cloud Dataproc, Cloud Dataflow, Cloud Sub/Pub, Hadoop ecosystem, Apache Spark, Apache Beam, and Apache Kafka). It also focuses on online versus batch prediction and job orchestration & automation;
  • Select the Relevant Storage Technologies: The considerations for this area include mapping storage systems to the business needs, data modeling, distributed systems, as well as tradeoffs, involving transactions, throughput, and latency;

Career Opportunities

The certified individuals can explore a variety of job opportunities. Some of the positions that they can take up include a Software Engineer, a Cloud Architect, a Data Engineer, a Sales Engineer, a Data Scientist, a Cloud Developer, and a Kubernetes Architect, among others. The salary outlook for these job roles is an average of $128,500 per annum.

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

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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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