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Google GCP-DE Exam Syllabus Topics:

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

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                                  Google Data Engineer Sample Questions (Q59-Q64):

                                  NEW QUESTION # 59
                                  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: B


                                  NEW QUESTION # 60
                                  You need to choose a database for a new project that has the following requirements:
                                  Fully managed
                                  Able to automatically scale up
                                  Transactionally consistent
                                  Able to scale up to 6 TB
                                  Able to be queried using SQL Which database do you choose?

                                  Answer: A


                                  NEW QUESTION # 61
                                  Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

                                  Answer: B


                                  NEW QUESTION # 62
                                  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: B

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


                                  NEW QUESTION # 63
                                  Which of these sources can you not load data into BigQuery from?

                                  Answer: C

                                  Explanation:
                                  You can load data into BigQuery from a file upload, Google Cloud Storage, Google Drive, or Google Cloud Bigtable. It is not possible to load data into BigQuery directly from Google Cloud SQL. One way to get data from Cloud SQL to BigQuery would be to export data from Cloud SQL to Cloud Storage and then load it from there.
                                  Reference: https://cloud.google.com/bigquery/loading-data


                                  NEW QUESTION # 64
                                  ......

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