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How to Prepare For Google Professional Data Engineer Exam

Preparation Guide for Google Professional Data Engineer Exam

Introduction to Google Professional Data Engineer Exam

Google has established a path for IT professionals endorse as a Data Engineer on the GCP platform. This accreditation program gives Google cloud professionals a way to endorse their skills. The evaluation relies on a meticulous exam using industry standard methodology to conclude whether or not a aspirant meets Google's proficiency standards.

The Professional Data Engineer exam assesses your ability to:

  • Ensure solution quality
  • Design data processing systems
  • Operationalize machine learning models
  • Build and operationalize data processing systems

Google Professional Data Engineer Exam certification is evidence of your skills, expertise in those areas in which you like to work. If candidate wants to work on Google Professional Data Engineer and prove his knowledge, Certification offered by Google. This Google Professional Data Engineer Certification helps a candidate to validates his skills in Big Data and Data engineering Technology.

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Understanding functional and technical aspects of Google Professional Data Engineer Exam Ensuring solution quality

The following will be discussed here:

  • Data security (encryption, key management)
  • Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))
  • Ensuring scalability and efficiency
  • Designing for security and compliance
  • Building and running test suites
  • Assessing, troubleshooting, and improving data representations and data processing infrastructure
  • Pipeline monitoring (e.g., Stackdriver)
  • Designing for data and application portability (e.g., multi-cloud, data residency requirements)
  • Resizing and autoscaling resources
  • Verification and monitoring
  • Ensuring flexibility and portability
  • Identity and access management (e.g.,Cloud IAM)
  • Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
  • Mapping to current and future business requirements
  • Choosing between ACID, idempotent, eventually consistent requirements
  • Ensuring reliability and fidelity
  • Ensuring privacy (e.g., Data Loss Prevention API)
  • Performing data preparation and quality control (e.g., Cloud Dataprep)
  • Data staging, cataloging, and discovery

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

Google Professional-Data-Engineer日本語 Exam Syllabus Topics:

SectionWeightObjectives
Operationalizing machine learning models26%- ML pipeline integration
  • 1. Feature engineering and feature stores
    • 2. Vertex AI pipeline deployment
      - Model deployment and monitoring
      • 1. Online vs batch prediction
        • 2. Model monitoring and drift detection
          Ensuring solution quality28%- Reliability and performance
          • 1. Fault tolerance and recovery strategies
            • 2. Monitoring pipelines and workloads
              - Security and governance
              • 1. Data governance and compliance
                • 2. IAM and access control in GCP
                  Building and operationalizing data processing systems24%- Data ingestion and integration
                  • 1. Batch ingestion pipelines (BigQuery, Cloud Storage)
                    • 2. Streaming ingestion (Pub/Sub, Dataflow)
                      - Data processing and transformation
                      • 1. Using Dataproc, Dataflow, and BigQuery SQL
                        • 2. ETL/ELT pipeline design
                          Designing data processing systems22%- Batch and streaming data processing design
                          • 1. Event-driven vs batch architectures
                            • 2. Latency, throughput, and consistency trade-offs
                              - Data architecture and storage design
                              • 1. Designing scalable and cost-effective data models
                                • 2. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)

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