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Google GCP-DE Exam Syllabus Topics:

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

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                                  Google Data Engineer Sample Questions (Q11-Q16):

                                  NEW QUESTION # 11
                                  You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

                                  Answer: C


                                  NEW QUESTION # 12
                                  Which of the following is NOT true about Dataflow pipelines?

                                  Answer: D

                                  Explanation:
                                  Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs Reference: https://cloud.google.com/dataflow/


                                  NEW QUESTION # 13
                                  Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

                                  Answer: A,B,C

                                  Explanation:
                                  Cloud Dataproc provides out-of-the box and end-to-end support for many of the most popular job types, including Spark, Spark SQL, PySpark, MapReduce, Hive, and Pig jobs.
                                  Reference: https://cloud.google.com/dataproc/docs/resources/faq#what_type_of_jobs_can_i_run


                                  NEW QUESTION # 14
                                  You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
                                  Executing the transformations on a schedule
                                  Enabling non-developer analysts to modify transformations
                                  Providing a graphical tool for designing transformations
                                  What should you do?

                                  Answer: F


                                  NEW QUESTION # 15
                                  You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?

                                  Answer: A


                                  NEW QUESTION # 16
                                  ......

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