君はまずネットで無料なGoogleのProfessional-Data-Engineer勉強資料試験問題をダウンロードしてから 弊社の品質を確信してから、購入してください。Omgzlookは提供した商品は君の成功を全力で助けさしたげます。 OmgzlookのGoogleのProfessional-Data-Engineer勉強資料試験トレーニング資料を利用して気楽に試験に合格しました。それはコストパフォーマンスが非常に高い資料ですから、もしあなたも私と同じIT夢を持っていたら、OmgzlookのGoogleのProfessional-Data-Engineer勉強資料試験トレーニング資料を利用してください。 しかし必ずしも大量の時間とエネルギーで復習しなくて、弊社が丹精にできあがった問題集を使って、試験なんて問題ではありません。
Google Cloud Certified Professional-Data-Engineer あなたの夢は何ですか。
Google Cloud Certified Professional-Data-Engineer勉強資料 - Google Certified Professional Data Engineer Exam Omgzlookが持つべきなIT問題集を提供するサイトでございます。 OmgzlookのProfessional-Data-Engineer テストサンプル問題教材を購入したら、あなたは一年間の無料アップデートサービスを取得しました。試験問題集が更新されると、Omgzlookは直ちにあなたのメールボックスにProfessional-Data-Engineer テストサンプル問題問題集の最新版を送ります。
IT業の多くの人がいくつか認証試験にパスしたくて、それなりの合格証明書が君に最大な上昇空間を与えます。この競争の激しい業界でとんとん拍子に出世させるのはGoogleのProfessional-Data-Engineer勉強資料認定試験ですが、簡単にパスではありません。でもたくさんの方法があって、最も少ない時間をエネルギーをかかるのは最高です。
Google Professional-Data-Engineer勉強資料 - でないと、絶対後悔しますよ。
我々のGoogleのProfessional-Data-Engineer勉強資料ソフトを利用してお客様の高通過率及び我々の技術の高いチームで、我々は自信を持って我々Omgzlookは専門的なのだと言えます。アフターサービスは会社を評価する重要な基準です。これをよくできるために、我々は全日24時間のサービスを提供します。GoogleのProfessional-Data-Engineer勉強資料ソフトを購入してから一年間の無料更新サービスも提供します。試験に失敗したら、全額で返金する承諾があります。だから、GoogleのProfessional-Data-Engineer勉強資料試験に合格したいあなたは安心で弊社の商品を選べばいいんです。
GoogleのProfessional-Data-Engineer勉強資料の認定試験の認可を取ったら、あなたは望むキャリアを得ることができるようになります。OmgzlookのGoogleのProfessional-Data-Engineer勉強資料試験トレーニング資料を利用したら、望むことを取得できます。
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