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Professional-Data-Engineer最新関連参考書 & Professional-Data-Engineer模擬試験最新版 - Professional-Data-Engineer認定資格試験問題集 - Royalholidayclubbed
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Royalholidayclubbedの専門家チームがGoogleのProfessional-Data-Engineer最新関連参考書認証試験に対して最新の短期有効なトレーニングプログラムを研究しました。GoogleのProfessional-Data-Engineer最新関連参考書「Google Certified Professional Data Engineer Exam」認証試験に参加者に対して30時間ぐらいの短期の育成訓練でらくらくに勉強しているうちに多くの知識を身につけられます。 Royalholidayclubbedの素晴らしい問題集はIT技術者が長年を重ねて、総括しました経験と結果です。先人の肩の上に立って、あなたも成功に一歩近付くことができます。 きっと君に失望させないと信じています。
Google Cloud Certified Professional-Data-Engineer こうしてGoogle認定試験がとても重要になります。
真剣にRoyalholidayclubbedのGoogle Professional-Data-Engineer - Google Certified Professional Data Engineer Exam最新関連参考書問題集を勉強する限り、受験したい試験に楽に合格することができるということです。 Royalholidayclubbedの商品を使用したあとのひとはRoyalholidayclubbedの商品がIT関連認定試験に対して役に立つとフィードバックします。弊社が提供した商品を利用すると試験にたやすく合格しました。
がむしゃらに試験に関連する知識を勉強しているのですか。それとも、効率が良い試験Professional-Data-Engineer最新関連参考書参考書を使っているのですか。Googleの認証資格は最近ますます人気になっていますね。
Google Professional-Data-Engineer最新関連参考書 - まだ何を待っていますか。
GoogleのProfessional-Data-Engineer最新関連参考書認定試験を受けることを決めたら、Royalholidayclubbedがそばにいて差し上げますよ。Royalholidayclubbedはあなたが自分の目標を達成することにヘルプを差し上げられます。あなたがGoogleのProfessional-Data-Engineer最新関連参考書「Google Certified Professional Data Engineer Exam」認定試験に合格する需要を我々はよく知っていますから、あなたに高品質の問題集と科学的なテストを提供して、あなたが気楽に認定試験に受かることにヘルプを提供するのは我々の約束です。
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Professional-Data-Engineer PDF DEMO:
QUESTION NO: 1
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: 2
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: 3
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: 4
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: 5
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
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Updated: May 27, 2022
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