높은통과율Professional-Data-Engineer시험대비최신덤프문제시험대비공부자료

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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Building and operationalizing data processing systems24%- Data processing and transformation
  • 1. ETL/ELT pipeline design
    • 2. Using Dataproc, Dataflow, and BigQuery SQL
      - Data ingestion and integration
      • 1. Batch ingestion pipelines (BigQuery, Cloud Storage)
        • 2. Streaming ingestion (Pub/Sub, Dataflow)
          Designing data processing systems22%- Batch and streaming data processing design
          • 1. Latency, throughput, and consistency trade-offs
            • 2. Event-driven vs batch architectures
              - Data architecture and storage design
              • 1. Designing scalable and cost-effective data models
                • 2. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)
                  Operationalizing machine learning models26%- Model deployment and monitoring
                  • 1. Online vs batch prediction
                    • 2. Model monitoring and drift detection
                      - ML pipeline integration
                      • 1. Vertex AI pipeline deployment
                        • 2. Feature engineering and feature stores
                          Ensuring solution quality28%- Security and governance
                          • 1. Data governance and compliance
                            • 2. IAM and access control in GCP
                              - Reliability and performance
                              • 1. Monitoring pipelines and workloads
                                • 2. Fault tolerance and recovery strategies

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                                  최신 Google Cloud Certified Professional-Data-Engineer 무료샘플문제 (Q220-Q225):

                                  질문 # 220
                                  An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
                                  They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

                                  정답：B

                                  설명：
                                  Explanation/Reference: https://cloud.google.com/solutions/business-intelligence/


                                  질문 # 221
                                  You need to choose a database for a new project that has the following requirements:
                                  - Fully managed
                                  - Able to automatically scale up
                                  - Transactionally consistent
                                  - Able to scale up to 6 TB
                                  - Able to be queried using SQL
                                  Which database do you choose?

                                  정답：B

                                  설명：
                                  It asks for scaling up which can be done in cloud sql, horizontal scaling is not possible in cloud sql Automatic storage increase If you enable this setting, Cloud SQL checks your available storage every 30 seconds. If the available storage falls below a threshold size, Cloud SQL automatically adds additional storage capacity. If the available storage repeatedly falls below the threshold size, Cloud SQL continues to add storage until it reaches the maximum of 30 TB.


                                  질문 # 222
                                  You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time.
                                  Which two methods can accomplish this? Choose 2 answers.

                                  정답：D,E


                                  질문 # 223
                                  You have several Spark jobs that run on a Cloud Dataproc cluster on a schedule. Some of the jobs run in sequence, and some of the jobs run concurrently. You need to automate this process. What should you do?

                                  정답：C

                                  설명：
                                  References:


                                  질문 # 224
                                  For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

                                  정답：B

                                  설명：
                                  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.


                                  질문 # 225
                                  ......

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