Exam Code: Professional-Data-Engineer
Exam Name: Google Certified Professional Data Engineer Exam
Certification Provider: Google
Corresponding Certification: Google Cloud Certified
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Google Cloud Big Data & Machine Learning Fundamentals course

This course is a gateway to introduce you to Google Cloud's big data and different machine learning functions. However, to successfully pass this training, you have to attain one year of experience in SQL, extract transform, data modeling, machine learning, programming in Python, and load activities. So, the objectives of the course are the following:

  • Recognize the purpose of the key Big data and Machine Learning products in Google Cloud
  • Utilize Cloud SQL & Dataproc to migrate existing MySQL, Pig, Spark, or Hive workloads to Google Cloud
  • Hire BigQuery and Cloud SQL for interactive data analysis
  • Create ML models using BigQuery ML, APIs, and AutoML.

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

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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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