而Goldmile-Infobiz是IT專業人士的最佳選擇，獲得Professional-Data-Engineer Pdf題庫認證是IT職業發展的有力保證，我們高品質的題庫能幫助你做到這一點。Professional-Data-Engineer Pdf題庫考試題庫也會不定期的更新，為你提供最有效的學習資料。使用我們的Professional-Data-Engineer Pdf題庫考試題庫進行考前復習，可以節約你大量的學習時間和費用，這是最適合獲得Professional-Data-Engineer Pdf題庫認證的所必須的學習資料。 它是你通過Professional-Data-Engineer Pdf題庫考試的保障。想要這個資料嗎？那就快點擊Goldmile-Infobiz的網站來購買吧。 Goldmile-Infobiz有最好品質最新的Google Professional-Data-Engineer Pdf題庫認證考試相關培訓資料，能幫你順利通過Google Professional-Data-Engineer Pdf題庫認證考試。
Google Cloud Certified Professional-Data-Engineer Goldmile-Infobiz可以幫助你實現這一願望。
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很多公司都招聘IT人才，他們一般考察IT人才的能力會參考他們擁有的IT相關認證證書，所以擁有一些IT相關的認證證書是受很多公司歡迎的。但是這些認證證書也不是很容易就能拿到的。Google Professional-Data-Engineer Pdf題庫 就是一個相當有難度的認證考試，雖然很多人報名參加Google Professional-Data-Engineer Pdf題庫考試，但是通過率並不是很高。
Google Professional-Data-Engineer Pdf題庫 - 這也導致在IT行業工作的人越來越多。
通過Professional-Data-Engineer Pdf題庫認證考試好像是一件很難的事情。已經報名參加考試的你，現在正在煩惱應該怎麼準備考試嗎？如果是這樣的話，請看下面的內容，我現在告訴你通過Professional-Data-Engineer Pdf題庫考試的捷徑。可以讓你一次就通過考試的優秀的Professional-Data-Engineer Pdf題庫考試資料出現了。它就是Goldmile-Infobiz的Professional-Data-Engineer Pdf題庫考古題。如果你想輕鬆通過考試，那麼快來試試吧。
這是為了考生們特別製作的考試資料。它可以讓你在短時間內充分地準備考試，並且輕鬆地通過考試。
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
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: 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
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
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Updated: May 27, 2022