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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. Vertex AI integration for pipelines
        • 2. BigQuery ML usage
          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. 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
                          Topic 4: Operationalizing data and ML pipelines30%- Monitoring and troubleshooting
                          • 1. Logging and observability
                            • 2. Performance optimization and debugging
                              - Pipeline automation and orchestration
                              • 1. Cloud Composer workflows
                                • 2. Scheduling and monitoring pipelines

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                                  Google Data Engineer Sample Questions (Q29-Q34):

                                  NEW QUESTION # 29
                                  When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.

                                  Answer: C

                                  Explanation:
                                  When using Cloud Dataproc clusters, configure your browser to use the SOCKS proxy. The SOCKS proxy routes data intended for the Cloud Dataproc cluster through an SSH tunnel.
                                  Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces


                                  NEW QUESTION # 30
                                  You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

                                  Answer: D


                                  NEW QUESTION # 31
                                  What are the minimum permissions needed for a service account used with Google Dataproc?

                                  Answer: A

                                  Explanation:
                                  Service accounts authenticate applications running on your virtual machine instances to other Google Cloud Platform services. For example, if you write an application that reads and writes files on Google Cloud Storage, it must first authenticate to the Google Cloud Storage API. At a minimum, service accounts used with Cloud Dataproc need permissions to read and write to Google Cloud Storage, and to write to Google Cloud Logging.
                                  Reference: https://cloud.google.com/dataproc/docs/concepts/service-accounts#important_notes


                                  NEW QUESTION # 32
                                  You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?

                                  Answer: B


                                  NEW QUESTION # 33
                                  Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

                                  Answer: C,D

                                  Explanation:
                                  The following rules will apply when you use preemptible workers with a Cloud Dataproc cluster: Processing only-Since preemptibles can be reclaimed at any time, preemptible workers do not store data.
                                  Preemptibles added to a Cloud Dataproc cluster only function as processing nodes.
                                  No preemptible-only clusters-To ensure clusters do not lose all workers, Cloud Dataproc cannot create preemptible-only clusters.
                                  Persistent disk size-As a default, all preemptible workers are created with the smaller of 100GB or the primary worker boot disk size. This disk space is used for local caching of data and is not available through HDFS.
                                  The managed group automatically re-adds workers lost due to reclamation as capacity permits. Reference:
                                  https://cloud.google.com/dataproc/docs/concepts/preemptible-vms


                                  NEW QUESTION # 34
                                  ......

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