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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Building and operationalizing data processing systems30%- Data pipeline implementation
  • 1. Dataproc and Spark-based processing
    • 2. Dataflow pipeline development
      - Data ingestion and transformation
      • 1. Pub/Sub streaming ingestion
        • 2. ETL/ELT workflows
          Designing data processing systems20%- Storage and data modeling
          • 1. Data lake architecture on Google Cloud Storage
            • 2. Data warehouse design using BigQuery
              - Data pipeline architecture design
              • 1. Scalable data ingestion design
                • 2. Batch vs streaming data processing selection
                  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
                          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

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                                  Google Data Engineer Sample Questions (Q49-Q54):

                                  NEW QUESTION # 49
                                  You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

                                  Answer: G


                                  NEW QUESTION # 50
                                  What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

                                  Answer: B

                                  Explanation:
                                  A general guide is to, keep your row keys reasonably short. Long row keys take up additional memory and storage and increase the time it takes to get responses from the Cloud Bigtable server.
                                  Reference: https://cloud.google.com/bigtable/docs/schema-design#row-keys


                                  NEW QUESTION # 51
                                  Flowlogistic's CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they've purchased a visualization tool to simplify the creation of BigQuery reports. However, they've been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

                                  Answer: B


                                  NEW QUESTION # 52
                                  Which of these sources can you not load data into BigQuery from?

                                  Answer: B

                                  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 # 53
                                  You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name Passing Certification Exams Made Easy visit - https://www.2PassEasy.com from the data source and then write to an output PCollection.
                                  Tom,555 X street Tim,553 Y street Sam, 111 Z street
                                  Which operation is best suited for the above data processing requirement?

                                  Answer: C

                                  Explanation:
                                  In Google Cloud dataflow SDK, you can use the ParDo to extract only a customer name of each element in your PCollection.
                                  Reference: https://cloud.google.com/dataflow/model/par-do


                                  NEW QUESTION # 54
                                  ......

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