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Professional-Data-Engineer最新受験攻略 & Professional-Data-Engineer問題例 - Professional-Data-Engineer認定資格試験問題集 - Royalholidayclubbed
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人に引けをとりたくないあなたはGoogle Professional-Data-Engineer最新受験攻略資格認定を取得したいですか。ここで、彼らはProfessional-Data-Engineer最新受験攻略試験にうまく合格できる秘訣は我々社の提供する質高いGoogle Professional-Data-Engineer最新受験攻略問題集を利用したことだと教えます。弊社のGoogle Professional-Data-Engineer最新受験攻略問題集を通して復習してから、真実的に自分の能力の向上を感じ、Professional-Data-Engineer最新受験攻略資格認定を受け取ります。 我々の提供するPDF版のGoogleのProfessional-Data-Engineer最新受験攻略試験の資料はあなたにいつでもどこでも読めさせます。我々もオンライン版とソフト版を提供します。 Professional-Data-Engineer最新受験攻略問題集は全面的かつわかりやすいです。
Google Cloud Certified Professional-Data-Engineer 試験に失敗したら、全額で返金いたします。
Google Cloud Certified Professional-Data-Engineer最新受験攻略 - Google Certified Professional Data Engineer Exam どんな業界で自分に良い昇進機会があると希望する職人がとても多いと思って、IT業界にも例外ではありません。 君が後悔しないようにもっと少ないお金を使って大きな良い成果を取得するためにRoyalholidayclubbedを選択してください。Royalholidayclubbedはまた一年間に無料なサービスを更新いたします。
Royalholidayclubbedの専門家チームが君の需要を満たすために自分の経験と知識を利用してGoogleのProfessional-Data-Engineer最新受験攻略認定試験対策模擬テスト問題集が研究しました。模擬テスト問題集と真実の試験問題がよく似ています。一目でわかる最新の出題傾向でわかりやすい解説と充実の補充問題があります。
それはRoyalholidayclubbedのGoogle Professional-Data-Engineer最新受験攻略問題集です。
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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 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: 4
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: 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
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