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Professional-Data-Engineer試験勉強攻略、Professional-Data-Engineer試験関連赤本

今この競争社会では、専門の技術があったら大きく優位を占めることができます。IT業界では関連の認証を持っているのは知識や経験の一つ証明でございます。Jpshikenが提供した問題集を使用してIT業界の頂点の第一歩としてとても重要な地位になります。君の夢は1歩更に近くなります。資料を提供するだけでなく、GoogleのProfessional-Data-Engineer試験も一年の無料アップデートになっています。

人生のチャンスを掴むことができる人は殆ど成功している人です。ですから、ぜひJpshikenというチャンスを掴んでください。JpshikenのGoogleのProfessional-Data-Engineer試験トレーニング資料はあなたがGoogleのProfessional-Data-Engineer認定試験に合格することを助けます。この認証を持っていたら、あなたは自分の夢を実現できます。そうすると人生には意義があります。

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Professional-Data-Engineer試験関連赤本 & Professional-Data-Engineer参考資料

簡単になりたい場合は、Professional-Data-Engineer信頼性の高い試験ガイドのバージョンを選択するのが難しいと感じる場合、PDFバージョンが適している可能性があります。 PDFバージョンは通常のファイルです。多くの受験者は、Professional-Data-Engineer信頼できる試験ガイドを紙に印刷してから読み書きすることに慣れています。はい、それは静かで明確です。また、不明な点がある場合は、他の人に簡単に質問したり話したりできます。他の人は、それが通常は練習資料だと考えるかもしれません。また、Google Professional-Data-Engineer信頼できる試験ガイドの多くのコピーを印刷して、他の人と共有することもできます。

Google Professional-Data-Engineer試験は、Google Cloud Platformで作業するプロフェッショナルのスキルと知識をテストするために設計されています。データ処理システムの設計、構築、管理の経験があり、機械学習モデルやデータ分析ツールを扱う人々に向けたものです。この試験は、ビジネスのニーズに応えるためにGoogle Cloud Platformツールを使用してソリューションを開発・実装する能力をプロフェッショナルが持っているかどうかをテストするために設計されています。

Google Professional-Data-Engineer認定試験は、かなりの量の準備が必要な厳格な試験です。候補者は、ビッグデータソリューションを扱う豊富な経験を持ち、データ処理と分析の最新の傾向に精通している必要があります。この認定は業界で高く評価されており、新しいキャリアの機会とより高い給与につながる可能性があります。

Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q165-Q170):

質問 # 165
Your company's data platform ingests CSV file dumps of booking and user profile data from upstream sources into Cloud Storage. The data analyst team wants to join these datasets on the email field available in both the datasets to perform analysis. However, personally identifiable information (PII) should not be accessible to the analysts. You need to de-identify the email field in both the datasets before loading them into BigQuery for analysts. What should you do?

A. 1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud DLP with format-preserving encryption with FFX as the de-identification transformation type.2. Load the booking and user profile data into a BigQuery table.
B. 1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.2.
Create a policy tag with the email mask as the data masking rule.3. Assign the policy to the email field in both tables. A4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts.
C. 1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.2.Create a policy tag with the default masking value as the data masking rule.3. Assign the policy to the email field in both tables.4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts
D. 1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud Data Loss Prevention (Cloud DLP) with masking as the de-identification transformations type.2. Load the booking and user profile data into a BigQuery table.

正解：A

解説：
Cloud DLP is a service that helps you discover, classify, and protect your sensitive data. It supports various de-identification techniques, such as masking, redaction, tokenization, and encryption. Format-preserving encryption (FPE) with FFX is a technique that encrypts sensitive data while preserving its original format and length. This allows you to join the encrypted data on the same field without revealing the actual values. FPE with FFX also supports partial encryption, which means you can encrypt only a portion of the data, such as the domain name of an email address. By using Cloud DLP to de-identify the email field with FPE with FFX, you can ensure that the analysts can join the booking and user profile data on the email field without accessing the PII. You can create a pipeline to de-identify the email field by usingrecordTransformations in Cloud DLP, which allows you to specify the fields and the de-identification transformations to apply to them.
You can then load the de-identified data into a BigQuery table for analysis. References:
De-identify sensitive data | Cloud Data Loss Prevention Documentation
Format-preserving encryption with FFX | Cloud Data Loss Prevention Documentation De-identify and re-identify data with the Cloud DLP API De-identify data in a pipeline

質問 # 166
A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time.
This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning. Over time, the query processing time has increased. You need to implement a change that would improve query performance in BigQuery. What should you do?

A. Tier older data onto Cloud Storage files, and leverage extended tables.
B. Implement clustering in BigQuery on the ingest date column.
C. Re-create the table using data partitioning on the package delivery date.
D. Implement clustering in BigQuery on the package-tracking ID column.

正解：D

質問 # 167
You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

A. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuery. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour. If that number falls below 4000, send an alert.
B. Consume the stream of data in Cloud Dataflow using Kafka IO. Set a fixed time window of 1 hour.
Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
C. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtable. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour. If that number falls below 4000, send an alert.
D. Consume the stream of data in Cloud Dataflow using Kafka IO. Set a sliding time window of 1 hour every 5 minutes. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.

正解：C

質問 # 168
Your company currently runs a large on-premises cluster using Spark Hive and Hadoop Distributed File System (HDFS) in a colocation facility. The duster is designed to support peak usage on the system, however, many jobs are batch n nature, and usage of the cluster fluctuates quite dramatically.
Your company is eager to move to the cloud to reduce the overhead associated with on-premises infrastructure and maintenance and to benefit from the cost savings. They are also hoping to modernize their existing infrastructure to use more servers offerings m order to take advantage of the cloud Because of the tuning of their contract renewal with the colocation facility they have only 2 months for their initial migration How should you recommend they approach thee upcoming migration strategy so they can maximize their cost savings in the cloud will still executing the migration in time?

A. Modernize the Spark workload for Dataflow and the Hive workload for BigQuery
B. Migrate the workloads to Dataproc plus Cloud Storage modernize later
C. Migrate the Spark workload to Dataproc plus HDFS, and modernize the Hive workload for BigQuery
D. Migrate the workloads to Dataproc plus HOPS, modernize later

正解：A

質問 # 169
Which Google Cloud Platform service is an alternative to Hadoop with Hive?

A. BigQuery
B. Cloud Datastore
C. Cloud Bigtable
D. Cloud Dataflow

正解：A

解説：
Explanation
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis.
Google BigQuery is an enterprise data warehouse.
Reference: https://en.wikipedia.org/wiki/Apache_Hive

質問 # 170
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Professional-Data-Engineer試験関連赤本: https://www.jpshiken.com/Professional-Data-Engineer_shiken.html

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