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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Building and operationalizing data processing systems30%- Data ingestion and transformation
  • 1. Pub/Sub streaming ingestion
    • 2. ETL/ELT workflows
      - Data pipeline implementation
      • 1. Dataproc and Spark-based processing
        • 2. Dataflow pipeline development
          Topic 2: Maintaining and optimizing data and ML solutions20%- Machine learning integration
          • 1. Vertex AI integration for pipelines
            • 2. BigQuery ML usage
              - Security and governance
              • 1. IAM and access control
                • 2. Data encryption and compliance
                  Topic 3: Designing data processing systems20%- Data pipeline architecture design
                  • 1. Scalable data ingestion design
                    • 2. Batch vs streaming data processing selection
                      - Storage and data modeling
                      • 1. Data lake architecture on Google Cloud Storage
                        • 2. Data warehouse design using BigQuery
                          Topic 4: Operationalizing data and ML pipelines30%- Monitoring and troubleshooting
                          • 1. Performance optimization and debugging
                            • 2. Logging and observability
                              - Pipeline automation and orchestration
                              • 1. Cloud Composer workflows
                                • 2. Scheduling and monitoring pipelines

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                                  Google Data Engineer Sample Questions (Q15-Q20):

                                  NEW QUESTION # 15
                                  You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the dat a. Which two actions should you take? (Choose two.)

                                  Answer: D,E


                                  NEW QUESTION # 16
                                  You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

                                  Answer: A

                                  Explanation:
                                  Reference:
                                  https://cloud.google.com/blog/products/gcp/using-bigquery-and-firebase-analytics-to-understandyour-mobile-ap


                                  NEW QUESTION # 17
                                  Cloud Dataproc is a managed Apache Hadoop and Apache service.

                                  Answer: C

                                  Explanation:
                                  Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.
                                  Reference: https://cloud.google.com/dataproc/docs/


                                  NEW QUESTION # 18
                                  You are developing a software application using Google's Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline. Which component will be used for the data processing operation?

                                  Answer: A

                                  Explanation:
                                  In Google Cloud, the Dataflow SDK provides a transform component. It is responsible for the data processing operation. You can use conditional, for loops, and other complex programming structure to create a branching pipeline.
                                  Reference: https://cloud.google.com/dataflow/model/programming-model


                                  NEW QUESTION # 19
                                  You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.

                                  Answer: B,C


                                  NEW QUESTION # 20
                                  ......

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