Shobhadoshi的產品Shobhadoshi的專家針對Google Professional-Data-Engineer題庫資料 認證考試研究出來的，是品質很高的產品。Shobhadoshi是一個很好的為Google Professional-Data-Engineer題庫資料 認證考試提供方便的網站。Shobhadoshi提供的產品能夠幫助IT知識不全面的人通過難的Google Professional-Data-Engineer題庫資料 認證考試。 如果你選擇了Shobhadoshi提供的測試練習題和答案，我們會給你提供一年的免費線上更新服務。Shobhadoshi可以100%保證你通過考試，如果你的考試未通過，我們將全額退款給你。 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。

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它就是Shobhadoshi的Google Professional-Data-Engineer題庫資料考古題。

我們都很清楚 Google Professional-Data-Engineer題庫資料 認證考試在IT行業中的地位是駐足輕重的地位，但關鍵的問題是能夠拿到Google Professional-Data-Engineer題庫資料的認證證書不是那麼簡單的。我們很清楚地知道網上缺乏有高品質的準確性高的相關考試資料。Shobhadoshi的考試練習題和答案可以為一切參加IT行業相關認證考試的人提供一切所急需的資料。它能時時刻刻地提供你們想要的資料，購買我們所有的資料能保證你通過你的第一次Google Professional-Data-Engineer題庫資料認證考試。

你不可能找到比它更好的考試相關的資料了。這個考古題可以讓你更準確地瞭解考試的出題點，從而讓你更有目的地學習相關知識。

Professional-Data-Engineer PDF DEMO:

QUESTION NO: 1
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: 2
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

QUESTION NO: 3
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: 4
You need to create a near real-time inventory dashboard that reads the main inventory tables in your BigQuery data warehouse. Historical inventory data is stored as inventory balances by item and location. You have several thousand updates to inventory every hour. You want to maximize performance of the dashboard and ensure that the data is accurate. What should you do?
A. Use the BigQuery streaming the stream changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
B. Use the BigQuery bulk loader to batch load inventory changes into a daily inventory movement table.
Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
C. Leverage BigQuery UPDATE statements to update the inventory balances as they are changing.
D. Partition the inventory balance table by item to reduce the amount of data scanned with each inventory update.
Answer: C

QUESTION NO: 5
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

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Updated: May 27, 2022