Professional-Data-Engineer過去問題 & Google Certified Professional-Data-Engineer Examテストトレーニング - Royalholidayclubbed

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我々社のGoogle　Professional-Data-Engineer過去問題問題集を購入するかどうかと疑問があると、弊社RoyalholidayclubbedのProfessional-Data-Engineer過去問題問題集のサンプルをしてみるのもいいことです。試用した後、我々のProfessional-Data-Engineer過去問題問題集はあなたを試験に順調に合格させると信じられます。なぜと言うのは、我々社の専門家は改革に応じて問題の更新と改善を続けていくのは出発点から勝つからです。我々Professional-Data-Engineer過去問題問題集の通過率は高いので、90％の合格率を保証します。あなたは弊社の高品質Google　Professional-Data-Engineer過去問題試験資料を利用して、一回に試験に合格します。 IT領域により良く発展したいなら、Google　Professional-Data-Engineer過去問題のような試験認定資格を取得するのは重要なことです。

また、Professional-Data-Engineer過去問題問題集は的中率が高いです。

多分、Professional-Data-Engineer - Google Certified Professional Data Engineer Exam過去問題テスト質問の数が伝統的な問題の数倍である。簡単で順調にGoogle Professional-Data-Engineer 教育資料認定試験を通すのは問題になりますが、Royalholidayclubbedはこの問題を解決できるよ。弊社の問題集はIT技術者がこつこつ研究して、正確で最新なもので君のProfessional-Data-Engineer 教育資料認定試験を簡単に通すことにいいトレーニングツールになりますよ。

GoogleのProfessional-Data-Engineer過去問題の認定試験に合格すれば、就職機会が多くなります。この試験に合格すれば君の専門知識がとても強いを証明し得ます。GoogleのProfessional-Data-Engineer過去問題の認定試験は君の実力を考察するテストでございます。

Google Professional-Data-Engineer過去問題 - それは受験者にとって重要な情報です。

我々は受験生の皆様により高いスピードを持っているかつ効率的なサービスを提供することにずっと力を尽くしていますから、あなたが貴重な時間を節約することに助けを差し上げます。Royalholidayclubbed GoogleのProfessional-Data-Engineer過去問題試験問題集はあなたに問題と解答に含まれている大量なテストガイドを提供しています。インターネットで時勢に遅れないProfessional-Data-Engineer過去問題勉強資料を提供するというサイトがあるかもしれませんが、Royalholidayclubbedはあなたに高品質かつ最新のGoogleのProfessional-Data-Engineer過去問題トレーニング資料を提供するユニークなサイトです。Royalholidayclubbedの勉強資料とGoogleのProfessional-Data-Engineer過去問題に関する指導を従えば、初めてGoogleのProfessional-Data-Engineer過去問題認定試験を受けるあなたでも一回で試験に合格することができます。

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Professional-Data-Engineer PDF DEMO:

QUESTION NO: 1
You have Cloud Functions written in Node.js that pull messages from Cloud Pub/Sub and send the data to BigQuery. You observe that the message processing rate on the Pub/Sub topic is orders of magnitude higher than anticipated, but there is no error logged in Stackdriver Log Viewer. What are the two most likely causes of this problem? Choose 2 answers.
A. Publisher throughput quota is too small.
B. The subscriber code cannot keep up with the messages.
C. The subscriber code does not acknowledge the messages that it pulls.
D. Error handling in the subscriber code is not handling run-time errors properly.
E. Total outstanding messages exceed the 10-MB maximum.
Answer: B,D

QUESTION NO: 2
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?
A. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
B. Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query
BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
C. Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
D. Load the data every 30 minutes into a new partitioned table in BigQuery.
Answer: D

QUESTION NO: 3
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. Preemptible workers cannot use persistent disk.
D. If a preemptible worker is reclaimed, then a replacement worker must be added manually.
Answer: A,B
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

QUESTION NO: 4
MJTelco is building a custom interface to share data. They have these requirements:
* They need to do aggregations over their petabyte-scale datasets.
* They need to scan specific time range rows with a very fast response time (milliseconds).
Which combination of Google Cloud Platform products should you recommend?
A. Cloud Datastore and Cloud Bigtable
B. Cloud Bigtable and Cloud SQL
C. BigQuery and Cloud Bigtable
D. BigQuery and Cloud Storage
Answer: C

QUESTION NO: 5
You have a query that filters a BigQuery table using a WHERE clause on timestamp and ID columns. By using bq query - -dry_run you learn that the query triggers a full scan of the table, even though the filter on timestamp and ID select a tiny fraction of the overall data. You want to reduce the amount of data scanned by BigQuery with minimal changes to existing SQL queries. What should you do?
A. Recreate the table with a partitioning column and clustering column.
B. Create a separate table for each I
C. Use the LIMIT keyword to reduce the number of rows returned.
D. Use the bq query - -maximum_bytes_billed flag to restrict the number of bytes billed.
Answer: C

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Updated: May 27, 2022

by RHC.

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