是不是還在為怎樣有把握地通過Google Professional-Data-Engineer考題套裝 認證考試而煩惱？你有想過選擇一個針對性的培訓嗎？選擇好的培訓可以有效的幫助你快速鞏固關IT方面的大量知識，讓你可以為Google Professional-Data-Engineer考題套裝 認證考試做好充分的準備。 Goldmile-Infobiz的專家團隊利用自己的經驗和知識不斷努力地研究，終於開發出了關於Google Professional-Data-Engineer考題套裝 認證考試的針對性的培訓資料，可以有效的幫助你為Google Professional-Data-Engineer考題套裝 認證考試做好充分的準備。Goldmile-Infobiz提供的培訓資料將是你的最佳選擇。 不需要大量的時間和金錢，僅需30個小時左右的特殊培訓，你就能輕鬆通過你的第一次參加的Google Professional-Data-Engineer考題套裝 認證考試。Goldmile-Infobiz能為你提供與真實的考試題目有緊密相似性的考試練習題。 我們網站是答案轉儲的領先供應商，我們有你們需要的最新最準確的考試認證培訓資料，也就是答案和考題。
Google Cloud Certified Professional-Data-Engineer 它可以讓你得到事半功倍的結果。
Google Cloud Certified Professional-Data-Engineer考題套裝 - Google Certified Professional Data Engineer Exam 或者你也可以選擇為你免費更新考試考古題。 因為Goldmile-Infobiz不但給你提供最好的資料，而且為你提供最優質的服務。如果你對我們的產品有任何意見都可以隨時提出，因為我們不僅以讓廣大考生輕鬆通過考試為宗旨，更把為大家提供最好的服務作為我們的目標。
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但是報名參加Google Professional-Data-Engineer考題套裝 認證考試是個明智的選擇，因為在如今競爭激烈的IT行業應該要不斷的提升自己。但是您可以選擇很多方式幫你通過考試。
Professional-Data-Engineer PDF DEMO:
QUESTION NO: 1
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: 2
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: 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
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: 5
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
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Updated: May 27, 2022