Professional-Data-Engineer日本語問題集 & Google Certified Professional-Data-Engineer Exam模擬体験 - Royalholidayclubbed

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Google Cloud Certified Professional-Data-Engineer 暇の時間を利用して勉強します。

Royalholidayclubbedが提供したGoogleのProfessional-Data-Engineer - Google Certified Professional Data Engineer Exam日本語問題集の試験トレーニング資料は受験生の皆さんの評判を得たのはもうずっと前のことになります。多分、Professional-Data-Engineer 復習教材テスト質問の数が伝統的な問題の数倍である。Google　Professional-Data-Engineer 復習教材試験参考書は全ての知識を含めて、全面的です。

例外がないです。いまRoyalholidayclubbedを選んで、あなたが始めたいトレーニングを選んで、しかも次のテストに受かったら、最も良いソース及び市場適合性と信頼性を得ることができます。RoyalholidayclubbedのGoogleのProfessional-Data-Engineer日本語問題集問題集と解答はProfessional-Data-Engineer日本語問題集認定試験に一番向いているソフトです。

Google Professional-Data-Engineer日本語問題集 - これは人の心によることです。

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日本語問題集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日本語問題集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日本語問題集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日本語問題集に関する指導を従えば、初めてGoogleのProfessional-Data-Engineer日本語問題集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。

この問題集は実際試験に出る可能性があるすべての問題を含んでいます。したがって、この問題集をまじめに勉強する限り、試験に合格することが朝飯前のことになることができます。

Professional-Data-Engineer PDF DEMO:

QUESTION NO: 1
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: 2
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

QUESTION NO: 3
You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
* The user profile: What the user likes and doesn't like to eat
* The user account information: Name, address, preferred meal times
* The order information: When orders are made, from where, to whom
The database will be used to store all the transactional data of the product. You want to optimize the data schema. Which Google Cloud Platform product should you use?
A. BigQuery
B. Cloud Datastore
C. Cloud SQL
D. Cloud Bigtable
Answer: A

QUESTION NO: 4
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: 5
Your startup has never implemented a formal security policy. Currently, everyone in the company has access to the datasets stored in Google BigQuery. Teams have freedom to use the service as they see fit, and they have not documented their use cases. You have been asked to secure the data warehouse. You need to discover what everyone is doing. What should you do first?
A. Use the Google Cloud Billing API to see what account the warehouse is being billed to.
B. Use Stackdriver Monitoring to see the usage of BigQuery query slots.
C. Get the identity and access management IIAM) policy of each table
D. Use Google Stackdriver Audit Logs to review data access.
Answer: B

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Updated: May 27, 2022

by RHC.

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