Free GCP-DE Exam Dumps | GCP-DE Exam Cram Questions

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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Operationalizing data and ML pipelines30%- Monitoring and troubleshooting
  • 1. Performance optimization and debugging
    • 2. Logging and observability
      - Pipeline automation and orchestration
      • 1. Scheduling and monitoring pipelines
        • 2. Cloud Composer workflows
          Topic 2: Maintaining and optimizing data and ML solutions20%- Machine learning integration
          • 1. BigQuery ML usage
            • 2. Vertex AI integration for pipelines
              - Security and governance
              • 1. Data encryption and compliance
                • 2. IAM and access control
                  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: 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

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                                  GCP-DE Exam Cram Questions - GCP-DE Passleader Review

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                                  Google Data Engineer Sample Questions (Q22-Q27):

                                  NEW QUESTION # 22
                                  You architect a system to analyze seismic dat
                                  a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

                                  Answer: A


                                  NEW QUESTION # 23
                                  Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

                                  Answer: A,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 # 24
                                  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 # 25
                                  Cloud Dataproc is a managed Apache Hadoop and Apache service.

                                  Answer: A

                                  Explanation:
                                  Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.
                                  Reference: https://cloud.google.com/dataproc/docs/


                                  NEW QUESTION # 26
                                  You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

                                  Answer: D


                                  NEW QUESTION # 27
                                  ......

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