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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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
          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: Maintaining and optimizing data and ML solutions20%- Machine learning integration
                  • 1. Vertex AI integration for pipelines
                    • 2. BigQuery ML usage
                      - Security and governance
                      • 1. Data encryption and compliance
                        • 2. IAM and access control
                          Topic 4: 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 lake architecture on Google Cloud Storage
                                • 2. Data warehouse design using BigQuery

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                                  Google Data Engineer Sample Questions (Q41-Q46):

                                  NEW QUESTION # 41
                                  As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.

                                  Answer: C,F


                                  NEW QUESTION # 42
                                  The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster .

                                  Answer: A

                                  Explanation:
                                  The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster master node. The cluster master-host-name is the name of your Cloud Dataproc cluster followed by an -m suffix-for example, if your cluster is named "my-cluster", the master-host-name would be "my-cluster-m". Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces


                                  NEW QUESTION # 43
                                  What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?

                                  Answer: C

                                  Explanation:
                                  Triggers control when the elements for a specific key and window are output. As elements arrive, they are put into one or more windows by a Window transform and its associated WindowFn, and then passed to the associated Trigger to determine if the Windows contents should be output.
                                  Reference:
                                  https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/transforms/windowing/Tri


                                  NEW QUESTION # 44
                                  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 # 45
                                  You need to choose a database for a new project that has the following requirements:
                                  Fully managed
                                  Able to automatically scale up
                                  Transactionally consistent
                                  Able to scale up to 6 TB
                                  Able to be queried using SQL Which database do you choose?

                                  Answer: A


                                  NEW QUESTION # 46
                                  ......

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