GCP-DE考題資訊 - GCP-DE題庫資料

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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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 2: Maintaining and optimizing data and ML solutions20%- Machine learning integration
          • 1. Vertex AI integration for pipelines
            • 2. BigQuery ML usage
              - Security and governance
              • 1. IAM and access control
                • 2. Data encryption and compliance
                  Topic 3: 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
                          Topic 4: Building and operationalizing data processing systems30%- Data pipeline implementation
                          • 1. Dataproc and Spark-based processing
                            • 2. Dataflow pipeline development
                              - Data ingestion and transformation
                              • 1. ETL/ELT workflows
                                • 2. Pub/Sub streaming ingestion

                                  >> GCP-DE考題資訊 <<

                                  Google GCP-DE題庫資料，GCP-DE題庫分享

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                                  最新的 Google Cloud Certified GCP-DE 免費考試真題 (Q12-Q17):

                                  問題 #12
                                  You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are: Decoupling producer from consumer Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely Near real-time SQL query Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?

                                  答案：E


                                  問題 #13
                                  Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

                                  答案：A,C,D

                                  解題說明：
                                  Cloud Dataproc provides out-of-the box and end-to-end support for many of the most popular job types, including Spark, Spark SQL, PySpark, MapReduce, Hive, and Pig jobs.
                                  Reference: https://cloud.google.com/dataproc/docs/resources/faq#what_type_of_jobs_can_i_run


                                  問題 #14
                                  Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

                                  答案：C,D

                                  解題說明：
                                  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


                                  問題 #15
                                  You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

                                  答案：A


                                  問題 #16
                                  Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
                                  SELECT person FROM `project1.example.table1` WHERE city = "London"
                                  How would you correct the error?

                                  答案：A

                                  解題說明：
                                  To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma. Reference:
                                  https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#nested_repeated_resu


                                  問題 #17
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