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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
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
          Building and operationalizing data processing systems30%- Data ingestion and transformation
          • 1. ETL/ELT workflows
            • 2. Pub/Sub streaming ingestion
              - Data pipeline implementation
              • 1. Dataflow pipeline development
                • 2. Dataproc and Spark-based processing
                  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
                          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. Batch vs streaming data processing selection
                                • 2. Scalable data ingestion design

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                                  Google GCP-DE Exam Cram | GCP-DE PDF Questions

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                                  Google Data Engineer Sample Questions (Q12-Q17):

                                  NEW QUESTION # 12
                                  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 # 13
                                  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: B

                                  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 # 14
                                  Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

                                  Answer: B,C,D

                                  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 # 15
                                  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: D,E


                                  NEW QUESTION # 16
                                  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: A,B


                                  NEW QUESTION # 17
                                  ......

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