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Google Professional-Data-Engineer試験は、データエンジニアリングに従事するプロフェッショナル向けにGoogleが提供する認定試験です。この試験は、Google Cloud Platformツールとサービスを使用したデータエンジニアリングの知識とスキルを試験するように設計されています。試験は、Google Cloud Platformテクノロジーを使用してデータ処理システムを設計、構築、運用、およびセキュリティを確保する能力を候補者が確認することを意図しています。

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Professional-Data-Engineerトレーニング資料は、ユーザーが学習した内容を統合し、多くのトレーニングの瞬間に追加するのに役立つように設計されています。ユーザーは、学習コンテンツの一部を終えた後、学習効果を時間内にテストできます。 Professional-Data-Engineerガイドトレントのトピックを使用して、ユーザーがこの機能の知識の弱点を見つけ、一定の練習を繰り返して、最終的に高い成功率を達成できるようにします。その結果、当社のProfessional-Data-Engineer試験問題は、ユーザーがProfessional-Data-Engineer試験に合格するための知識を習得できるように、実践内容の完全なセットを形成するように設計されています。

Google Professional-Data-Engineer認定を達成することは、データの専門家が専門知識を実証し、キャリアを促進するための優れた方法です。この認定は雇用主によって高く評価されており、新しい雇用機会とより高い給与につながる可能性があります。さらに、信頼性が高く、スケーラブルで、安全なデータ処理システムを設計、構築、維持するために必要なスキルと知識を個人に提供します。

Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q297-Q302):

質問 # 297
You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
* Executing the transformations on a schedule
* Enabling non-developer analysts to modify transformations
* Providing a graphical tool for designing transformations
What should you do?

A. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformation. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
B. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
C. Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.
Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery
D. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard schema. Merge the transformed tables together with a SQL query

正解：B

質問 # 298
Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use Hadoop jobs they have already created and minimize the management of the cluster as much as possible. They also want to be able to persist data beyond the life of the cluster. What should you do?

A. Create a Hadoop cluster on Google Compute Engine that uses Local SSD disks.
B. Create a Google Cloud Dataflow job to process the data.
C. Create a Google Cloud Dataproc cluster that uses persistent disks for HDFS.
D. Create a Hadoop cluster on Google Compute Engine that uses persistent disks.
E. Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector.

正解：B

質問 # 299
You are building a data pipeline on Google Cloud. You need to prepare data using a casual method for a machine-learning process. You want to support a logistic regression model. You also need to monitor and adjust for null values, which must remain real-valued and cannot be removed. What should you do?

A. Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 'none' using a Cloud Dataproc job.
B. Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 0 using a Cloud Dataprep job.
C. Use Cloud Dataflow to find null values in sample source data. Convert all nulls to 'none' using a Cloud Dataprep job.
D. Use Cloud Dataflow to find null values in sample source data. Convert all nulls to 0 using a custom script.

正解：C

質問 # 300
You use a dataset in BigQuery for analysis. You want to provide third-party companies with access to the same dataset. You need to keep the costs of data sharing low and ensure that the data is current. Which solution should you choose?

A. Create a Cloud Dataflow job that reads the data in frequent time intervals, and writes it to the relevant BigQuery dataset or Cloud Storage bucket for third-party companies to use.
B. Create an authorized view on the BigQuery table to control data access, and provide third-party companies with access to that view.
C. Use Cloud Scheduler to export the data on a regular basis to Cloud Storage, and provide third-party companies with access to the bucket.
D. Create a separate dataset in BigQuery that contains the relevant data to share, and provide third-party companies with access to the new dataset.

正解：C

解説：
Explanation

質問 # 301
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

A. A Dataproc cluster cannot have only preemptible workers.
B. Preemptible workers cannot store data.
C. If a preemptible worker is reclaimed, then a replacement worker must be added manually.
D. Preemptible workers cannot use persistent disk.

正解：A、B

解説：
Explanation
The following rules will apply when you use preemptible workers with a Cloud Dataproc cluster:
Processing only-Since preemptibles can be reclaimed at any time, preemptible workers do not store data.
Preemptibles added to a Cloud Dataproc cluster only function as processing nodes.
No preemptible-only clusters-To ensure clusters do not lose all workers, Cloud Dataproc cannot create preemptible-only clusters.
Persistent disk size-As a default, all preemptible workers are created with the smaller of 100GB or the primary worker boot disk size. This disk space is used for local caching of data and is not available through HDFS.
The managed group automatically re-adds workers lost due to reclamation as capacity permits.
Reference: https://cloud.google.com/dataproc/docs/concepts/preemptible-vms

質問 # 302
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権威のあるProfessional-Data-Engineer技術内容 &合格スムーズProfessional-Data-Engineer試験情報 |最高のProfessional-Data-Engineer基礎問題集

Professional-Data-Engineer試験情報 & Professional-Data-Engineer基礎問題集

Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q297-Q302):

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権威のあるProfessional-Data-Engineer技術内容 &合格スムーズProfessional-Data-Engineer試験情報 |最高のProfessional-Data-Engineer基礎問題集

Professional-Data-Engineer試験情報 & Professional-Data-Engineer基礎問題集

Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q297-Q302):