Professional-Data-Engineer的中関連問題 & Professional-Data-Engineer試験対応、Professional-Data-Engineer日本語対策 - Royalholidayclubbed

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我々の承諾だけでなく、お客様に最も全面的で最高のサービスを提供します。GoogleのProfessional-Data-Engineer的中関連問題の購入の前にあなたの無料の試しから、購入の後での一年間の無料更新まで我々はあなたのGoogleのProfessional-Data-Engineer的中関連問題試験に一番信頼できるヘルプを提供します。GoogleのProfessional-Data-Engineer的中関連問題試験に失敗しても、我々はあなたの経済損失を減少するために全額で返金します。 Royalholidayclubbedのトレーニング資料を選んだら、あなたは一生で利益を受けることができます。他のたくさんのトレーニング資料より、RoyalholidayclubbedのGoogleのProfessional-Data-Engineer的中関連問題試験トレーニング資料は一番良いものです。自分の能力を証明するために、Professional-Data-Engineer的中関連問題試験に合格するのは不可欠なことです。

Google Cloud Certified Professional-Data-Engineer 」とゴーリキーは述べました。

弊社のProfessional-Data-Engineer - Google Certified Professional Data Engineer Exam的中関連問題試験問題集によって、あなたの心と精神の満足度を向上させながら、勉強した後Professional-Data-Engineer - Google Certified Professional Data Engineer Exam的中関連問題試験資格認定書を受け取って努力する人生はすばらしいことであると認識られます。最近、GoogleのProfessional-Data-Engineer テスト内容試験は非常に人気のある認定試験です。あなたもこの試験の認定資格を取得したいのですか。

Google　Professional-Data-Engineer的中関連問題試験問題集を購買してから、一年間の無料更新を楽しみにしています。あなたにGoogle　Professional-Data-Engineer的中関連問題試験に関する最新かつ最完備の資料を勉強させ、試験に合格させることだと信じます。もしあなたはProfessional-Data-Engineer的中関連問題試験に合格しなかったら、全額返金のことを承諾します。

Google Professional-Data-Engineer的中関連問題 - 給料を倍増させることも不可能ではないです。

RoyalholidayclubbedのGoogleのProfessional-Data-Engineer的中関連問題の試験問題は同じシラバスに従って、実際のGoogleのProfessional-Data-Engineer的中関連問題認証試験にも従っています。弊社はずっとトレーニング資料をアップグレードしていますから、提供して差し上げた製品は一年間の無料更新サービスの景品があります。あなたはいつでもサブスクリプションの期間を延長することができますから、より多くの時間を取って充分に試験を準備できます。Royalholidayclubbedというサイトのトレーニング資料を利用するかどうかがまだ決まっていなかったら、Royalholidayclubbedのウェブで一部の試験問題と解答を無料にダウンローしてみることができます。あなたに向いていることを確かめてから買うのも遅くないですよ。あなたが決して後悔しないことを保証します。

ですから、IT業界で仕事している皆さんはGoogleの認定試験を受験して資格を取得することを通して、彼らの知識やスキルを向上させます。Professional-Data-Engineer的中関連問題認定試験はGoogleの最も重要な試験の一つです。

Professional-Data-Engineer PDF DEMO:

QUESTION NO: 1
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?
A. Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.
B. Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.
C. Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.
D. Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.
Answer: D
Explanation
It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance, If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud
Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
It is recommended to avoid creating your Compute Engine instance in a different region from your
Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.
Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance

QUESTION NO: 2
You have an Apache Kafka Cluster on-prem with topics containing web application logs. You need to replicate the data to Google Cloud for analysis in BigQuery and Cloud Storage. The preferred replication method is mirroring to avoid deployment of Kafka Connect plugins.
What should you do?
A. Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Sink connector. Use a Dataflow job to read fron PubSub and write to GCS.
B. Deploy a Kafka cluster on GCE VM Instances. Configure your on-prem cluster to mirror your topics to the cluster running in GCE. Use a Dataproc cluster or Dataflow job to read from Kafka and write to
GCS.
C. Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a
Source connector. Use a Dataflow job to read fron PubSub and write to GCS.
D. Deploy a Kafka cluster on GCE VM Instances with the PubSub Kafka connector configured as a Sink connector. Use a Dataproc cluster or Dataflow job to read from Kafka and write to GCS.
Answer: B

QUESTION NO: 3
Which Google Cloud Platform service is an alternative to Hadoop with Hive?
A. Cloud Datastore
B. Cloud Bigtable
C. BigQuery
D. Cloud Dataflow
Answer: C
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

QUESTION NO: 4
You want to use Google Stackdriver Logging to monitor Google BigQuery usage. You need an instant notification to be sent to your monitoring tool when new data is appended to a certain table using an insert job, but you do not want to receive notifications for other tables. What should you do?
A. Using the Stackdriver API, create a project sink with advanced log filter to export to Pub/Sub, and subscribe to the topic from your monitoring tool.
B. In the Stackdriver logging admin interface, enable a log sink export to Google Cloud Pub/Sub, and subscribe to the topic from your monitoring tool.
C. In the Stackdriver logging admin interface, and enable a log sink export to BigQuery.
D. Make a call to the Stackdriver API to list all logs, and apply an advanced filter.
Answer: C

QUESTION NO: 5
You need to create a near real-time inventory dashboard that reads the main inventory tables in your BigQuery data warehouse. Historical inventory data is stored as inventory balances by item and location. You have several thousand updates to inventory every hour. You want to maximize performance of the dashboard and ensure that the data is accurate. What should you do?
A. Use the BigQuery streaming the stream changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
B. Use the BigQuery bulk loader to batch load inventory changes into a daily inventory movement table.
Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
C. Leverage BigQuery UPDATE statements to update the inventory balances as they are changing.
D. Partition the inventory balance table by item to reduce the amount of data scanned with each inventory update.
Answer: C

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Updated: May 27, 2022

by RHC.

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