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Professional-Data-Engineer試題 & Professional-Data-Engineer學習指南 - Professional-Data-Engineer題庫資料 - Royalholidayclubbed
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Royalholidayclubbed是一個對Google Professional-Data-Engineer試題 認證考試提供針對性培訓的網站。Royalholidayclubbed也是一個不僅能使你的專業知識得到提升，而且能使你一次性通過Google Professional-Data-Engineer試題 認證考試的網站。Royalholidayclubbed提供的培訓資料是由很多IT資深專家不斷利用自己的經驗和知識研究出來的，品質很好，準確性很高。 Royalholidayclubbed Google的Professional-Data-Engineer試題考試培訓資料得到廣大考生的稱譽已經不是最近幾天的事情了，說明Royalholidayclubbed Google的Professional-Data-Engineer試題考試培訓資料信得過，確實可以幫助廣大考生通過考試，讓考生沒有後顧之憂，Royalholidayclubbed Google的Professional-Data-Engineer試題考試培訓資料暢銷和同行相比一直遙遙領先，率先得到廣大消費者的認可，口碑當然不用說，如果你要參加 Google的Professional-Data-Engineer試題考試，就趕緊進Royalholidayclubbed這個網站，相信你一定會得到你想要的，不會錯過就不會後悔，如果你想成為最專業最受人矚目的IT專家，那就趕緊加入購物車吧。 如果有我們的軟體有任何更新版本，都會立即推送給客戶。
Google Cloud Certified Professional-Data-Engineer 只有這樣，在考試的時候你才可以輕鬆應對。
Google Cloud Certified Professional-Data-Engineer試題 - Google Certified Professional Data Engineer Exam 用過了軟體版的考古題，你就可以在參加考試時以一種放鬆的心態來做題，有利於你正常發揮你的水準。 你可以免費下載考古題的一部分。在購買考古題之前，你可以去Royalholidayclubbed的網站瞭解更多的資訊，更好地瞭解這個網站。
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Royalholidayclubbed的Google Professional-Data-Engineer試題考古題是很好的參考資料。
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Royalholidayclubbed以它強大的考古題得到人們的認可，只要你選擇它作為你的考前復習工具，就會在Professional-Data-Engineer試題資格考試中有非常滿意的收穫，這也是大家有目共睹的。現在馬上去網站下載免費試用版本，你就會相信自己的選擇不會錯。
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
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: 4
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: 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
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