NewValidDumpsのGoogle　Professional-Data-Engineer合格体験談問題集は専門家たちが数年間で過去のデータから分析して作成されて、試験にカバーする範囲は広くて、受験生の皆様のお金と時間を節約します。我々Professional-Data-Engineer合格体験談問題集の通過率は高いので、90％の合格率を保証します。あなたは弊社の高品質Google　Professional-Data-Engineer合格体験談試験資料を利用して、一回に試験に合格します。 弊社の商品は試験の範囲を広くカバーすることが他のサイトがなかなか及ばならないです。それほかに品質はもっと高くてGoogleのProfessional-Data-Engineer合格体験談認定試験「Google Certified Professional Data Engineer Exam」の受験生が最良の選択であり、成功の最高の保障でございます。 努力すれば報われますなので、Google　Professional-Data-Engineer合格体験談資格認定を取得して自分の生活状況を改善できます。

Google Cloud Certified Professional-Data-Engineer きっと君に失望させないと信じています。

最近のわずかの数年間で、GoogleのProfessional-Data-Engineer - Google Certified Professional Data Engineer Exam合格体験談認定試験は日常生活でますます大きな影響をもたらすようになりました。 我々は受験生の皆様により高いスピードを持っているかつ効率的なサービスを提供することにずっと力を尽くしていますから、あなたが貴重な時間を節約することに助けを差し上げます。NewValidDumps GoogleのProfessional-Data-Engineer 問題サンプル試験問題集はあなたに問題と解答に含まれている大量なテストガイドを提供しています。

それはいくつかの理由があります。第一、NewValidDumpsは受験生の要求をよく知っています。しかも、他のどのサイトよりも良いサービスを提供します。

その中で、Google Professional-Data-Engineer合格体験談認定試験は最も重要な一つです。

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まだ何を待っていますか。早速買いに行きましょう。

Professional-Data-Engineer PDF DEMO:

QUESTION NO: 1
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: 2
Which Google Cloud Platform service is an alternative to Hadoop with Hive?
A. Cloud Datastore
B. Cloud Bigtable
C. BigQuery
D. Cloud Dataflow
Answer: C
Explanation
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis.
Google BigQuery is an enterprise data warehouse.
Reference: https://en.wikipedia.org/wiki/Apache_Hive

QUESTION NO: 3
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: 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 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

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Updated: May 27, 2022