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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Building and operationalizing data processing systems30%- Data ingestion and transformation
  • 1. ETL/ELT workflows
    • 2. Pub/Sub streaming ingestion
      - Data pipeline implementation
      • 1. Dataflow pipeline development
        • 2. Dataproc and Spark-based processing
          Operationalizing data and ML pipelines30%- Pipeline automation and orchestration
          • 1. Scheduling and monitoring pipelines
            • 2. Cloud Composer workflows
              - Monitoring and troubleshooting
              • 1. Logging and observability
                • 2. Performance optimization and debugging
                  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
                          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

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                                  Google Data Engineer Sample Questions (Q49-Q54):

                                  NEW QUESTION # 49
                                  You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

                                  Answer: B


                                  NEW QUESTION # 50
                                  The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster .

                                  Answer: D

                                  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 # 51
                                  MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

                                  Answer: D


                                  NEW QUESTION # 52
                                  Which of the following is NOT one of the three main types of triggers that Dataflow supports?

                                  Answer: A

                                  Explanation:
                                  There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers. You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way Reference: https://cloud.google.com/dataflow/model/triggers


                                  NEW QUESTION # 53
                                  You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
                                  The user profile: What the user likes and doesn't like to eat
                                  The user account information: Name, address, preferred meal times
                                  The order information: When orders are made, from where, to whom
                                  The database will be used to store all the transactional data of the product. You want to optimize the data schem a. Which Google Cloud Platform product should you use?

                                  Answer: A


                                  NEW QUESTION # 54
                                  ......

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