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Professional-Data-Engineer考試備考經驗，Professional-Data-Engineer熱門題庫 - Google Professional-Data-Engineer參考資料 - Royalholidayclubbed
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比賽是這樣，同樣考試也是這樣的。有很多人因為沒有充分的時間準備考試從而放棄了參加IT認證考試。但是，如果使用了好的資料，即使只有很短的時間來準備，你也完全可以以高分通過考試。 只為成功找方法，不為失敗找藉口。想要通過Google的Professional-Data-Engineer考試備考經驗考試認證其實也沒有那麼難，關鍵在於你用什麼樣的方式方法。 但是，和考試的重要性一樣，這個考試也是非常難的。
所以，Royalholidayclubbed的Professional-Data-Engineer考試備考經驗考古題吧。
Royalholidayclubbed的Professional-Data-Engineer - Google Certified Professional Data Engineer Exam考試備考經驗考古題有著讓你難以置信的命中率。 有了最新詳細的題庫和答案，為您的Professional-Data-Engineer 參考資料考試做好充分的準備，我們將保證您在考試中取得成功。在購買前，您還可以下載我們提供的Professional-Data-Engineer 參考資料免費DEMO來試用，這是非常有效的學習資料。
如果要說為什麼，那當然是因為Professional-Data-Engineer考試備考經驗考試是一個非常重要的考試。對IT職員來說，沒有取得這個資格那麼會對工作帶來不好的影響。這個考試的認證資格可以給你的工作帶來很多有益的幫助，也可以幫助你晉升。
通過Google Google Professional-Data-Engineer考試備考經驗認證考試可以給你帶來很多改變。
有很多途徑可以幫你通過Google Professional-Data-Engineer考試備考經驗 認證考試的，選擇好的途徑也就是選擇了好的保障。Royalholidayclubbed可以為你提供好的培訓工具，為您參加Google Professional-Data-Engineer考試備考經驗 認證考試提供高品質的參考資料。Royalholidayclubbed提供的考試練習題和答案是根據Google Professional-Data-Engineer考試備考經驗 認證考試的考試大綱研究出來的。所以Royalholidayclubbed提供的資料的品質很高，具有很高權威性，絕對可以盡全力幫你通過Google Professional-Data-Engineer考試備考經驗 認證考試。Royalholidayclubbed也會不斷提升更新我們提供的Google Professional-Data-Engineer考試備考經驗 認證考試資料，來滿足您的需求。
要想一次性通過Google Professional-Data-Engineer考試備考經驗 認證考試您必須得有一個好的準備和一個完整的知識結構。Royalholidayclubbed為你提供的資源正好可以完全滿足你的需求。
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
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: 3
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: 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 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
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Updated: May 27, 2022
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