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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. Dataproc and Spark-based processing
                • 2. Dataflow pipeline development
                  Maintaining and optimizing data and ML solutions20%- Security and governance
                  • 1. Data encryption and compliance
                    • 2. IAM and access control
                      - Machine learning integration
                      • 1. BigQuery ML usage
                        • 2. Vertex AI integration for pipelines
                          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. Batch vs streaming data processing selection
                                • 2. Scalable data ingestion design

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                                  Google Data Engineer Sample Questions (Q22-Q27):

                                  NEW QUESTION # 22
                                  MJTelco is building a custom interface to share dat
                                  a. They have these requirements:
                                  They need to do aggregations over their petabyte-scale datasets.
                                  They need to scan specific time range rows with a very fast response time (milliseconds). Which combination of Google Cloud Platform products should you recommend?

                                  Answer: B


                                  NEW QUESTION # 23
                                  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 # 24
                                  You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on-premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

                                  Answer: B


                                  NEW QUESTION # 25
                                  You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

                                  Answer: C

                                  Explanation:
                                  Reference: https://cloud.google.com/solutions/data-lifecycle-cloud-platform


                                  NEW QUESTION # 26
                                  What are two methods that can be used to denormalize tables in BigQuery?

                                  Answer: C

                                  Explanation:
                                  The conventional method of denormalizing data involves simply writing a fact, along with all its dimensions, into a flat table structure. For example, if you are dealing with sales transactions, you would write each individual fact to a record, along with the accompanying dimensions such as order and customer information. The other method for denormalizing data takes advantage of BigQuery's native support for nested and repeated structures in JSON or Avro input data. Expressing records using nested and repeated structures can provide a more natural representation of the underlying data. In the case of the sales order, the outer part of a JSON structure would contain the order and customer information, and the inner part of the structure would contain the individual line items of the order, which would be represented as nested, repeated elements.
                                  Reference: https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data


                                  NEW QUESTION # 27
                                  ......

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