ちなみに、ShikenPASS Professional-Data-Engineerの一部をクラウドストレージからダウンロードできます：https://drive.google.com/open?id=19qalaFn7GU8Yp3UG33A_aVQBqzFVMw_4
一つの試験だけでは多くの時間を無駄にする必要がありません。Professional-Data-Engineer認定試験が大変難しいと感じて、多くの時間を取らなければならないとしたら、ツールとしてShikenPASSのProfessional-Data-Engineer問題集を利用したほうがいいです。この問題集はあなたに時間を節約させることができますから。もっと重要なのは、この問題集はあなたが試験に合格することを保証できますから。この問題集よりもっと良いツールは何一つありません。試験の準備をするのにたくさんの時間を無駄にするより、そんな時間を利用してもっと有意義なことをしたほうがいいです。ですから、はやくShikenPASSのサイトに行ってもっと多くの情報を読みましょう。この素晴らしきチャンスを逃したらきっと後悔しますよ。
| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Professional Data Engineer Exam |
| Exam Number: | Professional-Data-Engineer |
| Real Exam Qty: | 40 - 50 |
| Available Languages: | English, Japanese |
| Passing Score: | 700 / 1000 |
| Exam Duration: | 120 minutes |
| Exam Price: | USD 200 (plus tax where applicable) |
| Exam Format: | Multiple choice, Multiple select |
| Certificate Validity Period: | 2 years |
| Related Certifications: | Google Cloud Associate Cloud Engineer Google Cloud Professional Cloud Architect Google Cloud Professional Data Analyst |
| Recommended Training: | Official Exam Guide Google Cloud Professional Data Engineer Learning Path |
| Exam Registration: | Google Cloud Certification Registration |
| Sample Questions: | Google Professional-Data-Engineer Sample Questions |
| Exam Way: | Online-proctored or onsite-proctored |
| Pre Condition: | No mandatory prerequisites; recommended 3+ years industry experience, including 1+ year designing and managing Google Cloud data solutions |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/data-engineer |
>> Professional-Data-Engineer関連日本語版問題集 <<
Google特別で適切に設計されたProfessional-Data-Engineer試験資料を所有しているだけでなく、想像を超えた幅広いサービスを提供できるため、人気があります。最初は、信頼できる制作チームがあり、Professional-Data-Engineer学習ガイドは何百人もの専門家によって改訂されています。つまり、シラバスと最新の変更に応じて、オーダーメイドのProfessional-Data-Engineer学習教材を受け取ることができます。理論とブレークスルーの開発。間違いなく、当社のProfessional-Data-Engineer練習トレントは最新の情報に対応しています。
Google Professional-Data-Engineer試験は、Google Cloudテクノロジーを使用したデータエンジニアリングにおける個人のスキルと知識を厳しくテストするものです。熟練したデータエンジニアリング専門家の需要が増えるにつれて、この認定は、業界で自分の地位を確立したい人にとって、多くの利益のある求人機会を提供することができます。認定プロセスには、実務経験、幅広い準備、そして今日の急速に進化するテクノロジー生態系で高く評価されるスキルを習得するための献身が必要です。
質問 # 220
Your company's on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage. You want to minimize the storage cost of the migration. What should you do?
正解：B
解説：
Explanation/Reference: https://cloud.google.com/dataproc/
質問 # 221
You have a Standard Tier Memorystore for Redis instance deployed in a production environment. You need to simulate a Redis instance failover in the most accurate disaster recovery situation, and ensure that the failover has no impact on production data. What should you do?
正解：A
解説：
To simulate a Redis instance failover in a production-like environment without impacting production data, the best approach is to use a development environment. Here's why option D is the best choice:
Standard Tier Memorystore for Redis:
The Standard Tier provides high availability and automatic failover capabilities. It's suitable for testing failover scenarios in a controlled environment.
Development Environment:
Using a development environment ensures that any potential data loss or impact from the failover simulation does not affect production data, maintaining the integrity and availability of the production system.
Limited-Data-Loss Mode:
The limited-data-loss mode for manual failover ensures that data loss is minimized during the failover process, making it a realistic simulation of a production failover scenario.
Steps to Implement:
Create a Development Environment:
Set up a development environment with a Standard Tier Memorystore for Redis instance that mirrors the configuration of your production instance.
Initiate Manual Failover:
Initiate a manual failover using the limited-data-loss data protection mode to simulate a failover scenario:
gcloud redis instances failover INSTANCE_ID --data-protection-mode=limited-data-loss Verify Failover:
Monitor and verify the failover process to ensure it behaves as expected, simulating the disaster recovery scenario accurately.
Reference Links:
Memorystore for Redis Documentation
Manual Failover in Memorystore
質問 # 222
You are preparing an organization-wide dataset. You need to preprocess customer data stored in a restricted bucket in Cloud Storage. The data will be used to create consumer analyses. You need to follow data privacy requirements, including protecting certain sensitive data elements, while also retaining all of the data for potential future use cases. What should you do?
正解：C
解説：
The core requirements are to protect sensitive data elements (data privacy) while retainingalldata for potential future use, and then using this preprocessed data for consumer analyses.
* Retaining All Data:This immediately makes option B (remove sensitive fields) unsuitable because it involves data loss.
* Protecting Sensitive Data for Analysis & Future Use:Masking is a de-identification technique that redacts or replaces sensitive data with a substitute, allowing the data structure and usability for analysis to be maintained without exposing the original sensitive values. This aligns with protecting data while still making it usable.
* Cloud Data Loss Prevention (DLP) API:This service is specifically designed to discover, classify, and protect sensitive data. It offers various de-identification techniques, including masking.
* Dataflow:This is a serverless, fast, and cost-effective service for unified stream and batch data processing. It's well-suited for transforming large datasets, such as those read from Cloud Storage, and can integrate with the DLP API for de-identification.
* Writing to BigQuery:BigQuery is an ideal destination for an organization-wide dataset for consumer analyses.
Therefore, using Dataflow to read the data from Cloud Storage, leveraging the Cloud DLP API tomask(a form of de-identification) the sensitive elements, and then writing the processed (masked) data to BigQuery is the most appropriate solution. This approach protects privacy for the consumer analyses dataset while the original, unaltered data can still be retained in the restricted Cloud Storage bucket for future use cases that might require access to the original sensitive information (under strict governance).
Let's analyze why other options are less suitable:
* Option B:"Remove sensitive fields" means data loss, which contradicts the requirement to retain all data for potential future use cases.
* Option C:Encrypting sensitive fields with Cloud KMS and writing them to BigQuery is a valid way to protect data. However, for "consumer analyses," masked data is generally more directly usable than encrypted data. Analysts would typically work with de-identified (e.g., masked) data rather than directly querying encrypted fields and managing decryption keys for analytical purposes. While decryption is possible, masking often provides a better balance of privacy and utility for broad analysis.
The question also implies creating a datasetforanalysis, where masking makes the data ready-to-use for that purpose. The original data remains in Cloud Storage.
* Option D:Using CMEK encrypts the entire object in Cloud Storage at rest. While this protects the data in Cloud Storage, federated queries from BigQuery would access the raw, unmasked data (assuming decryption occurs seamlessly). This doesn't address the preprocessing requirement of protectingcertain sensitive data elementswithin the data itself for theconsumer analysesdataset. The goal is to create a de- identified dataset for analysis, not just secure the raw data at rest.
Reference:
Google Cloud Documentation: Cloud Data Loss Prevention > De-identification overview. "De-identification is the process of removing identifying information from data. Cloud DLP uses de-identification techniques such as masking, tokenization, pseudonymization, date shifting, and more to help you protect sensitive data." Google Cloud Documentation: Cloud Data Loss Prevention > Basic de-identification > Masking. "Masking hides parts of data by replacing characters with a symbol, such as an asterisk (*) or hash (#)." Google Cloud Documentation: Dataflow > Overview. "Dataflow is a fully managed streaming analytics service that minimizes latency, processing time, and cost through autoscaling and batch processing." Google Cloud Solution: Automating the de-identification of PII in large-scale datasets using Cloud DLP and Dataflow. This solution guide explicitly outlines using Dataflow and DLP API for de-identifying (including masking) data from Cloud Storage and loading it into BigQuery. "You can use Cloud DLP to scan data for sensitive elements andthen apply de-identification techniques such as redaction, masking, or tokenization." and "This tutorial uses Dataflow to orchestrate the de-identification process."
質問 # 223
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?
正解：B
解説：
Explanation
質問 # 224
Your company's on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage.
You want to minimize the storage cost of the migration. What should you do?
正解：D
解説：
First rule of dataproc is to keep data in GCS.
質問 # 225
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Professional-Data-Engineer関連資格知識: https://www.shikenpass.com/Professional-Data-Engineer-shiken.html
P.S.ShikenPASSがGoogle Driveで共有している無料の2026 Google Professional-Data-Engineerダンプ：https://drive.google.com/open?id=19qalaFn7GU8Yp3UG33A_aVQBqzFVMw_4