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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
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. Using Dataproc, Dataflow, and BigQuery SQL
        • 2. ETL/ELT pipeline design
          Ensuring solution quality28%- Security and governance
          • 1. IAM and access control in GCP
            • 2. Data governance and compliance
              - Reliability and performance
              • 1. Monitoring pipelines and workloads
                • 2. Fault tolerance and recovery strategies
                  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
                          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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                                  Google Certified Professional Data Engineer Exam Sample Questions (Q74-Q79):

                                  NEW QUESTION # 74
                                  Which of the following are feature engineering techniques? (Select 2 answers)

                                  Answer: A,D

                                  Explanation:
                                  Selecting and crafting the right set of feature columns is key to learning an effective model. Bucketization is a process of dividing the entire range of a continuous feature into a set of consecutive bins/buckets, and then converting the original numerical feature into a bucket ID (as a categorical feature) depending on which bucket that value falls into. Using each base feature column separately may not be enough to explain the data. To learn the differences between different feature combinations, we can add crossed feature columns to the model.
                                  Reference:
                                  https://www.tensorflow.org/tutorials/wide#selecting_and_engineering_features_for_the_model


                                  NEW QUESTION # 75
                                  You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

                                  Answer: A


                                  NEW QUESTION # 76
                                  Does Dataflow process batch data pipelines or streaming data pipelines?

                                  Answer: D

                                  Explanation:
                                  Explanation
                                  Dataflow is a unified processing model, and can execute both streaming and batch data pipelines Reference: https://cloud.google.com/dataflow/


                                  NEW QUESTION # 77
                                  You are deploying an Apache Airflow directed acyclic graph (DAG) in a Cloud Composer 2 instance. You have incoming files in a Cloud Storage bucket that the DAG processes, one file at a time. The Cloud Composer instance is deployed in a subnetwork with no Internet access.
                                  Instead of running the DAG based on a schedule, you want to run the DAG in a reactive way every time a new file is received. What should you do?

                                  Answer: A


                                  NEW QUESTION # 78
                                  You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform.
                                  Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
                                  Which Google database service should you use?

                                  Answer: A


                                  NEW QUESTION # 79
                                  ......

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