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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Building and operationalizing data processing systems30%- Data pipeline implementation
  • 1. Dataflow pipeline development
    • 2. Dataproc and Spark-based processing
      - Data ingestion and transformation
      • 1. ETL/ELT workflows
        • 2. Pub/Sub streaming ingestion
          Topic 2: Operationalizing data and ML pipelines30%- Monitoring and troubleshooting
          • 1. Performance optimization and debugging
            • 2. Logging and observability
              - Pipeline automation and orchestration
              • 1. Cloud Composer workflows
                • 2. Scheduling and monitoring pipelines
                  Topic 3: 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
                          Topic 4: Maintaining and optimizing data and ML solutions20%- Security and governance
                          • 1. IAM and access control
                            • 2. Data encryption and compliance
                              - Machine learning integration
                              • 1. BigQuery ML usage
                                • 2. Vertex AI integration for pipelines

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                                  Google Data Engineer Sample Questions (Q33-Q38):

                                  NEW QUESTION # 33
                                  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 # 34
                                  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: D


                                  NEW QUESTION # 35
                                  You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

                                  Answer: A


                                  NEW QUESTION # 36
                                  When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.

                                  Answer: B

                                  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 # 37
                                  Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

                                  Answer: B


                                  NEW QUESTION # 38
                                  ......

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