最新のGCP-DE問題集で試験を準備する

Google GCP-DE 試験概要:

Google GCP-DE 試験シラバストピック:

Google Data Engineer 認定 GCP-DE 試験問題：

問題 #1

Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

A. Cloud Pub/Sub, Cloud Dataflow, and Local SSD
B. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
C. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
D. Cloud Pub/Sub, Cloud SQL, and Cloud Storage

問題 #2

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?

A. Cloud BigTable
B. Cloud Datastore
C. BigQuery
D. Cloud SQL

問題 #3

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?

A. Rowkey: data_pointColumn data: device_id, date
B. Rowkey: dateColumn data: device_id, data_point
C. Rowkey: date#data_pointColumn data: device_id
D. Rowkey: date#device_idColumn data: data_point
E. Rowkey: device_idColumn data: date, data_point

問題 #4

You work for a global shipping company. You want to train a model on 40 TB of data to predict which ships in each geographic region are likely to cause delivery delays on any given day. The model will be based on multiple attributes collected from multiple sources. Telemetry data, including location in GeoJSON format, will be pulled from each ship and loaded every hour. You want to have a dashboard that shows how many and which ships are likely to cause delays within a region. You want to use a storage solution that has native functionality for prediction and geospatial processing. Which storage solution should you use?

A. Cloud SQL for PostgreSQL
B. Cloud Datastore
C. BigQuery
D. Cloud Bigtable

問題 #5

Which of these sources can you not load data into BigQuery from?

A. Google Cloud Storage
B. File upload
C. Google Cloud SQL
D. Google Drive

解説：