Shobhadoshi的Google Professional-Data-Engineer參考資料 認證考試的考試練習題和答案是由我們的專家團隊利用他們的豐富的知識和經驗研究出來的，能充分滿足參加Google Professional-Data-Engineer參考資料 認證考試的考生的需求。你可能從相關的網站或書籍上也看到部分相關培訓材料，但是我們Shobhadoshi的Google Professional-Data-Engineer參考資料 認證考試的相關資料是擁最全面的，可以給你最好的保障。參加Google Professional-Data-Engineer參考資料 認證考試的考生請選擇Shobhadoshi為你提供的考試練習題和答案，因為它是你的最佳選擇。 一般如果你使用Shobhadoshi提供的針對性復習題，你可以100%通過Google Professional-Data-Engineer參考資料 認證考試。快將Shobhadoshi加入你的購物車吧! 下一個IT行業的成功人士說不定就是你。 如果你參加Google Professional-Data-Engineer參考資料認證考試，你選擇Shobhadoshi就是選擇成功！祝你好運。

而且通過 Google Professional-Data-Engineer參考資料 認證考試也不是很簡單的。

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因為這是國際廣泛認可的資格，因此參加Google的認證考試的人也越來越多了。其中，Professional-Data-Engineer參考資料認證考試就是最重要的考試之一。那麼，為了通過這個考試你是怎麼進行準備的呢？是死命地學習與考試相關的知識呢，還是使用了高效率的學習資料呢？

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Professional-Data-Engineer PDF DEMO:

QUESTION NO: 1
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: 2
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: 3
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: 4
You want to use Google Stackdriver Logging to monitor Google BigQuery usage. You need an instant notification to be sent to your monitoring tool when new data is appended to a certain table using an insert job, but you do not want to receive notifications for other tables. What should you do?
A. Using the Stackdriver API, create a project sink with advanced log filter to export to Pub/Sub, and subscribe to the topic from your monitoring tool.
B. In the Stackdriver logging admin interface, enable a log sink export to Google Cloud Pub/Sub, and subscribe to the topic from your monitoring tool.
C. In the Stackdriver logging admin interface, and enable a log sink export to BigQuery.
D. Make a call to the Stackdriver API to list all logs, and apply an advanced filter.
Answer: C

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