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隨著社會的發展，現在IT行業得到了人們的青睞，也有越來越多的人們想考取IT方面的資格認證證書，在事業上更進一步。這個時候你應該想到的是Royalholidayclubbed網站，它是你考試合格的好幫手。Royalholidayclubbed的強大考古題是IT技術專家們多年來總結出來的經驗和結果，站在這些前人的肩膀上，會讓你離成功更進一步。我們的IT精英團隊的力量會讓你難以置信。你可以先嘗試我們Royalholidayclubbed為你們提供的免費下載關於Google Professional-Data-Engineer考古题推薦認證考試的部分考題及答案來測我們的可靠性。如果使用我們的Professional-Data-Engineer考古题推薦考古題沒有通過考試，我們將無條件的退款。

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Professional-Data-Engineer考古题推薦是Google認證考試，所以通過Professional-Data-Engineer考古题推薦是踏上Google 認證的第一步。也因此Professional-Data-Engineer考古题推薦認證考試變得越來越火熱，參加Professional-Data-Engineer考古题推薦考試的人也越來越多，但是Professional-Data-Engineer考古题推薦認證考試的通過率並不是很高。當你選擇Professional-Data-Engineer考古题推薦考試時有沒有選擇相關的考試課程？

Google Professional-Data-Engineer考古题推薦 - 人之所以能，是相信能。

在21世紀這個IT行業如此輝煌的時代，競爭是很激烈的。理所當然的，在IT行業中Google Professional-Data-Engineer考古题推薦認證考試成為了一個很熱門的考試。報名參加考試的人越來越多，並且能通過這個認證考試也是那些雄心勃勃的IT專業人士的夢想。

因為這個考古題的命中率非常高，只要你認真記住考古題裏面出現的問題和答案，那麼你就可以通過Professional-Data-Engineer考古题推薦考試。你已經報名參加Google的Professional-Data-Engineer考古题推薦認證考試了嗎？“馬上就要到考試的時間了，但是我還是沒有信心通過考試，應該怎麼辦呢？有捷徑可以讓我順利通過考試嗎？看參考書的時間也不夠了。

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

by RHC.

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