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Google Professional-Data-Engineer certification exam is designed for professionals seeking to demonstrate their expertise in designing, building, and managing data processing systems on the Google Cloud Platform. Professional-Data-Engineer exam is intended for data engineers, data architects, and data analysts who work with big data solutions. Google Certified Professional Data Engineer Exam certification validates the skills and knowledge required to design and build data processing systems, as well as manage and monitor them in a production environment.

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Google Professional-Data-Engineer certification exam is a comprehensive exam that requires detailed knowledge of data engineering concepts and technologies. It is designed to assess the candidate's ability to apply this knowledge to real-world scenarios, and to design and implement solutions that meet the needs of a wide range of users. Professional-Data-Engineer exam is intended for professionals who have experience working with data engineering technologies and who are looking to advance their careers in this field.

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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

Google Professional-Data-Engineer certification has become a must-have for data engineering professionals who work with Google Cloud. Google Certified Professional Data Engineer Exam certification validates their knowledge and skills 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 also helps professionals to stand out in a competitive job market and advance their careers.

Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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. Publishing datasets
  • 2. Data sharing and collaboration
Topic 2: Designing data processing systems (~30% of the exam)30%- Designing data pipelines
  • 1. Processing logic
  • 2. Data acquisition and import
  • 3. Integrating with new data sources
  • 4. Streaming (e.g., windowing, late arriving data)
  • 5. Batch processing
  • 6. AI data enrichment
- Designing data processing resources
  • 1. Cost optimization
  • 2. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
  • 3. Cluster sizing and autoscaling
- Selecting appropriate storage technologies
  • 1. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB
  • 2. Mapping storage options to business requirements
Topic 3: Ingesting and processing the data (~20% of the exam)20%- Building and maintaining data structures and databases
  • 1. Defining data lifecycle
  • 2. Planning for analytical and operational use cases
- Performing security considerations
  • 1. Identity and Access Management (IAM)
  • 2. Data encryption
  • 3. Auditing, privacy, and compliance
- Deploying and operationalizing the pipelines
  • 1. Job automation and orchestration (Cloud Composer, Workflows)
  • 2. CI/CD for data pipelines
Topic 4: Maintaining and automating data workloads (~15% of the exam)15%- Automating data processes
  • 1. Scheduling jobs
  • 2. Continuous integration and continuous deployment (CI/CD)
  • 3. Workflow orchestration
- Monitoring data pipelines and data processes
  • 1. Logging, monitoring, and troubleshooting
  • 2. Managing quotas and resource usage
- Designing for reliability and fidelity
  • 1. Performing data quality and validation checks
  • 2. Recovering from failures
  • 3. Planning for monitoring and alerting
Topic 5: Storing the data (~20% of the exam)20%- Planning for using a data warehouse
  • 1. Mapping business requirements
  • 2. Designing the data model
  • 3. Deciding the degree of data normalization
  • 4. Defining architecture to support data access patterns
- 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. Analyzing data access patterns
  • 2. Lifecycle management of data
  • 3. Planning for storage costs and performance
- Using a data lake
  • 1. Processing data
  • 2. Monitoring the data lake
  • 3. Managing the lake (data discovery, access, cost controls)
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