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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Operationalizing data and ML pipelines30%- Monitoring and troubleshooting
  • 1. Logging and observability
    • 2. Performance optimization and debugging
      - Pipeline automation and orchestration
      • 1. Scheduling and monitoring pipelines
        • 2. Cloud Composer workflows
          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
                  Maintaining and optimizing data and ML solutions20%- Security and governance
                  • 1. IAM and access control
                    • 2. Data encryption and compliance
                      - Machine learning integration
                      • 1. Vertex AI integration for pipelines
                        • 2. BigQuery ML usage
                          Building and operationalizing data processing systems30%- Data ingestion and transformation
                          • 1. Pub/Sub streaming ingestion
                            • 2. ETL/ELT workflows
                              - Data pipeline implementation
                              • 1. Dataflow pipeline development
                                • 2. Dataproc and Spark-based processing

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                                  Google Data Engineer Sample Questions (Q57-Q62):

                                  NEW QUESTION # 57
                                  An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
                                  They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

                                  Answer: A


                                  NEW QUESTION # 58
                                  Which Google Cloud Platform service is an alternative to Hadoop with Hive?

                                  Answer: B

                                  Explanation:
                                  Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis. Google BigQuery is an enterprise data warehouse. Reference: https://en.wikipedia.org/wiki/Apache_Hive


                                  NEW QUESTION # 59
                                  You work for a global shipping company. You want to train a model on 40 TB of data to predict which ships in each geographic region are likely to cause delivery delays on any given day. The model will be based on multiple attributes collected from multiple sources. Telemetry data, including location in GeoJSON format, will be pulled from each ship and loaded every hour. You want to have a dashboard that shows how many and which ships are likely to cause delays within a region. You want to use a storage solution that has native functionality for prediction and geospatial processing. Which storage solution should you use?

                                  Answer: A


                                  NEW QUESTION # 60
                                  You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30-90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

                                  Answer: D


                                  NEW QUESTION # 61
                                  Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

                                  Answer: A,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 # 62
                                  ......

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