Professional-Data-Engineer認定に関する最高のGoogle Professional-Data-Engineer受験問題集

P.S. JPTestKingがGoogle Driveで共有している無料かつ新しいProfessional-Data-Engineerダンプ：https://drive.google.com/open?id=1A5Mz6ZQIdthmyD8fuzgW_dmCBgjy3Mbf

JPTestKing GoogleのProfessional-Data-Engineer試験問題集は実践の検査に合格しますから、広い研究と実際を基づいている経験を提供できます。JPTestKingはIT領域の10年以上の認定経験を持っていますから、問題と解答に含まれています。Professional-Data-Engineer試験に準備するためにインターネットで色々なトレーニングツールを見つけることができますが、JPTestKing のProfessional-Data-Engineer試験資料は最も良いトレーニング資料です。、弊社は最全面的な認証試験問題と解答を提供するだけでまく、一年間の無料更新サービスも提供いたします。

Google Professional-Data-Engineer試験は、選択式および複数選択式の問題、およびシナリオベースの問題から構成され、候補者が問題解決スキルを示す必要がある。試験時間は3時間で、合格には少なくとも70％のスコアが必要です。試験料は200ドルで、英語、日本語、スペイン語で利用可能です。

Google Professional-Data-Engineer認定試験は、データ処理システム、データモデリング、データ分析、データの視覚化、機械学習など、幅広いトピックを対象としています。 Googleクラウドプラットフォームの製品と、BigQuery、DataFlow、DataProc、Pub/Subなどのサービスを強く理解する必要があります。この試験では、スケーラブルで効率的で安全なソリューションを設計および実装する機能もテストします。

>> Professional-Data-Engineer日本語版復習資料 <<

Professional-Data-Engineerオンラインテストエンジン、Professional-Data-Engineerトレーニング資料、Professional-Data-Engineer試験合格率

Professional-Data-Engineerテストガイドの言語は理解しやすいため、学習障害のない学習者は、学生であろうと現職のスタッフであろうと、初心者であれ、多くの経験豊富な経験豊富なスタッフであれ、年。困難なテストを通過するためにProfessional-Data-Engineerガイドトレントを選択するのは素晴らしい素晴らしいアイデアです。全体として、信じられないことは何もありません。今から意味のある何かをするために、成功はheする人を待って、購入して行きません！

Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q151-Q156):

質問 # 151
You're using Bigtable for a real-time application, and you have a heavy load that is a mix of read and writes. You've recently identified an additional use case and need to perform hourly an analytical job to calculate certain statistics across the whole database. You need to ensure both the reliability of your production application as well as the analytical workload.
What should you do?

A. Add a second cluster to an existing instance with a single-cluster routing, use live-traffic app profile for your regular workload and batch-analytics profile for the analytics workload.
B. Increase the size of your existing cluster twice and execute your analytics workload on your new resized cluster.
C. Add a second cluster to an existing instance with a multi-cluster routing, use live-traffic app profile for your regular workload and batch-analytics profile for the analytics workload.
D. Export Bigtable dump to GCS and run your analytical job on top of the exported files.

正解：C

解説：
https://cloud.google.com/bigtable/docs/replication-settings#batch-vs-serve

質問 # 152
You are designing storage for 20 TB of text files as part of deploying a data pipeline on Google Cloud. Your input data is in CSV format. You want to minimize the cost of querying aggregate values for multiple users who will query the data in Cloud Storage with multiple engines. Which storage service and schema design should you use?

A. Use Cloud Storage for storage. Link as temporary tables in BigQuery for query.
B. Use Cloud Bigtable for storage. Install the HBase shell on a Compute Engine instance to query the Cloud Bigtable data.
C. Use Cloud Bigtable for storage. Link as permanent tables in BigQuery for query.
D. Use Cloud Storage for storage. Link as permanent tables in BigQuery for query.

正解：D

質問 # 153
You are running a streaming pipeline with Dataflow and are using hopping windows to group the data as the data arrives. You noticed that some data is arriving late but is not being marked as late data, which is resulting in inaccurate aggregations downstream. You need to find a solution that allows you to capture the late data in the appropriate window. What should you do?

A. Expand your hopping window so that the late data has more time to arrive within the grouping.
B. Use watermarks to define the expected data arrival window Allow late data as it arrives.
C. Change your windowing function to session windows to define your windows based on certain activity.
D. Change your windowing function to tumbling windows to avoid overlapping window periods.

正解：B

解説：
Watermarks are a way of tracking the progress of time in a streaming pipeline. They are used to determine when a window can be closed and the results emitted. Watermarks can be either event-time based or processing-time based. Event-time watermarks track the progress of time based on the timestamps of the data elements, while processing-time watermarks track the progress of time based on the system clock. Event-time watermarks are more accurate, but they require the data source to provide reliable timestamps. Processing-time watermarks are simpler, but they can be affected by system delays or backlogs.
By using watermarks, you can define the expected data arrival window for each windowing function. You can also specify how to handle late data, which is data that arrives after the watermark has passed. You can either discard late data, or allow late data and update the results as new data arrives. Allowing late data requires you to use triggers to control when the results are emitted.
In this case, using watermarks and allowing late data is the best solution to capture the late data in the appropriate window. Changing the windowing function to session windows or tumbling windows will not solve the problem of late data, as they still rely on watermarks to determine when to close the windows.
Expanding the hopping window might reduce the amount of late data, but it will also change the semantics of the windowing function and the results.
References:
* Streaming pipelines | Cloud Dataflow | Google Cloud
* Windowing | Apache Beam

質問 # 154
Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow. Numerous data logs are being are being generated during this step, and the team wants to analyze them. Due to the dynamic nature of the campaign, the data is growing exponentially every hour.
The data scientists have written the following code to read the data for a new key features in the logs.
BigQueryIO.Read
.named("ReadLogData")
.from("clouddataflow-readonly:samples.log_data")
You want to improve the performance of this data read. What should you do?

A. Call a transform that returns TableRow objects, where each element in the PCollexction represents a single row in the table.
B. Use of both the Google BigQuery TableSchema and TableFieldSchema classes.
C. Specify the TableReference object in the code.
D. Use .fromQuery operation to read specific fields from the table.

正解：A

質問 # 155
You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.
What should you do?

A. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query
request
B. Create groups for your users and give those groups access to the dataset
C. Create a dummy user and grant dataset access to that user. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
D. Create a service account and grant dataset access to that account. Use the service account's private key to access the dataset

正解：D

質問 # 156
......

なぜ我々のGoogleのProfessional-Data-Engineerソフトに自信があるかと聞かれたら、まずは我々JPTestKingの豊富な経験があるチームです、次は弊社の商品を利用してGoogleのProfessional-Data-Engineer試験に合格する多くのお客様です。GoogleのProfessional-Data-Engineer試験は国際的に認められてあなたはこの認証がほしいですか。弊社のGoogleのProfessional-Data-Engineer試験のソフトを通して、あなたはリラクスで得られます。

Professional-Data-Engineer試験参考書: https://www.jptestking.com/Professional-Data-Engineer-exam.html

BONUS！！！ JPTestKing Professional-Data-Engineerダンプの一部を無料でダウンロード：https://drive.google.com/open?id=1A5Mz6ZQIdthmyD8fuzgW_dmCBgjy3Mbf

Tony Young Tony Young

Biography

Professional-Data-Engineer認定に関する最高のGoogle Professional-Data-Engineer受験問題集

Professional-Data-Engineerオンラインテストエンジン、Professional-Data-Engineerトレーニング資料、Professional-Data-Engineer試験合格率

Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q151-Q156):

Quick Links