Google GCP-DE 試験概要:
| 認定ベンダー: | Google Cloud |
|---|---|
| 試験名: | Professional Data Engineer |
| 試験番号: | GCP-DE |
| 関連資格: | Google Cloud Certified Professional Data Engineer |
| 合格点: | 非公開 |
| 試験時間: | 120 分 |
| 対応言語: | 英語, 日本語 |
| 出題数: | 50-60 |
| 認定の有効期間: | 2年 |
| 試験形式: | 複数選択式, 択一式 |
| 受験料: | $200 USD |
| サンプル問題: | Google GCP-DE サンプル問題 |
| 受験方法: | オンライン監督試験または会場試験センター |
| 前提条件: | 正式な前提条件はありません。Googleは、Google Cloudを使用したソリューションの設計と管理に1年以上携わった経験を含む、3年以上の業界経験を推奨しています。 |
| 公式シラバスのURL: | https://cloud.google.com/learn/certification/data-engineer |
Google GCP-DE 試験シラバストピック:
| セクション | 比重 | 目標 |
|---|---|---|
| トピック 1: データ処理システムの構築と運用 | 28%-33% | - システムの運用
|
| トピック 2: ソリューション品質の確保 | 20%-25% | - データ品質管理
|
| トピック 3: データ処理システムの設計 | 22%-27% | - データパイプラインの設計
|
| トピック 4: ソリューションの管理と最適化 | 20%-25% | - 信頼性とスケーラビリティ
|
Google Data Engineer 認定 GCP-DE 試験問題：
問題 #1
Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer dat a. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?
A. Increase the size of the Google Persistent Disk on your server.
B. Increase your network bandwidth from Compute Engine to Cloud Storage.
C. Increase the CPU size on your server.
D. Increase your network bandwidth from your datacenter to GCP.
問題 #2
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 Pub/Sub, Cloud Dataflow, and Cloud Storage
C. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
D. Cloud Pub/Sub, Cloud SQL, and Cloud Storage
問題 #3
Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.
You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)
A. Introduce data compression for each file to increase the rate file of file transfer.
B. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
C. Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.
D. Assemble 1,000 files into a tape archive (TAR) fil
E. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
F. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
問題 #4
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?
A. Use Cloud SQL for storag
B. Use Cloud Dataflow to transform data to support query patterns.
C. Use Cloud Dataflow to transform data to support query patterns.
D. Add secondary indexes to support query patterns.
E. Use Cloud Spanner for storag
F. Use Cloud Spanner for storag
G. Add secondary indexes to support query patterns.
H. Use Cloud SQL for storag
問題 #5
You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?
A. Cloud Bigtable
B. Cloud Datastore
C. Cloud SQL
D. Cloud Spanner
解説：
| 問題 #1 正解： D | 問題 #2 正解： D | 問題 #3 正解： B、E | 問題 #4 正解： B | 問題 #5 正解： C |
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