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

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

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Ensure Solution Quality

  • Ensure Fidelity & Reliability: The applicants should be able to carry out data preparation & quality control (such as Cloud Dataprep), verify and monitor, as well as plan, execute, and stress test data recovery (including rerunning failed jobs, fault tolerance, and retrospective re-analysis performance). Besides that, they should be able to choose between idempotent ACID and eventual consistent prerequisites;
  • Ensure Efficiency & Scalability: The potential candidates will be required to demonstrate their ability to build and run test suits as well as monitor pipeline, including Stackdriver. It also focuses on their skills related to assessing, improving, and troubleshooting data process infrastructure and data representations. This area will also require that the test takers demonstrate the capacity to resize and autoscale resources;
  • Design for Compliance & Security: The consideration for this topic includes identity & access management such as Cloud IAM. You should also know about data security (including key management and encryption) and privacy assurance (such as Data Loss Prevention API). This part also covers the skills needed in legal compliance, including Health Insurance Portability & Accountability Act, FedRAMP, Children’s Online Privacy Protection Act, and General Data Protection Regulation;
  • Ensure Portability & Flexibility: The considerations for this domain include the design for application and data portability, including data residency prerequisites and Multiple-Cloud. It also coves data staging, discovery, and cataloging, as well as mapping to future and current business prerequisites.

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

SectionWeightObjectives
Topic 1: Operationalizing machine learning models26%- Model deployment and monitoring
  • 1. Online vs batch prediction
    • 2. Model monitoring and drift detection
      - ML pipeline integration
      • 1. Vertex AI pipeline deployment
        • 2. Feature engineering and feature stores
          Topic 2: Ensuring solution quality28%- Reliability and performance
          • 1. Monitoring pipelines and workloads
            • 2. Fault tolerance and recovery strategies
              - Security and governance
              • 1. IAM and access control in GCP
                • 2. Data governance and compliance
                  Topic 3: Building and operationalizing data processing systems24%- Data processing and transformation
                  • 1. Using Dataproc, Dataflow, and BigQuery SQL
                    • 2. ETL/ELT pipeline design
                      - Data ingestion and integration
                      • 1. Batch ingestion pipelines (BigQuery, Cloud Storage)
                        • 2. Streaming ingestion (Pub/Sub, Dataflow)
                          Topic 4: Designing data processing systems22%- Data architecture and storage design
                          • 1. Designing scalable and cost-effective data models
                            • 2. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)
                              - Batch and streaming data processing design
                              • 1. Event-driven vs batch architectures
                                • 2. Latency, throughput, and consistency trade-offs
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