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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. Performance optimization and debugging
        • 2. Logging and observability
          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 warehouse design using BigQuery
                • 2. Data lake architecture on Google Cloud Storage
                  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
                          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

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                                  Google Data Engineer Sample Questions (Q74-Q79):

                                  NEW QUESTION # 74
                                  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: A


                                  NEW QUESTION # 75
                                  You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
                                  Which Google database service should you use?

                                  Answer: D


                                  NEW QUESTION # 76
                                  For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

                                  Answer: A

                                  Explanation:
                                  It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance, If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
                                  It is recommended to avoid creating your Compute Engine instance in a different region from your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.
                                  Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance


                                  NEW QUESTION # 77
                                  You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

                                  Answer: B


                                  NEW QUESTION # 78
                                  All Google Cloud Bigtable client requests go through a front-end server they are sent to a Cloud Bigtable node.

                                  Answer: B

                                  Explanation:
                                  In a Cloud Bigtable architecture all client requests go through a front-end server before they are sent to a Cloud Bigtable node.
                                  The nodes are organized into a Cloud Bigtable cluster, which belongs to a Cloud Bigtable instance, which is a container for the cluster. Each node in the cluster handles a subset of the requests to the cluster.
                                  When additional nodes are added to a cluster, you can increase the number of simultaneous requests that the cluster can handle, as well as the maximum throughput for the entire cluster.
                                  Reference: https://cloud.google.com/bigtable/docs/overview


                                  NEW QUESTION # 79
                                  ......

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