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たくさんの品質高く問題集を取り除き、我々Goldmile-InfobizのProfessional-Data-Engineer - Google Certified Professional Data Engineer Exam試験概要問題集を選らんでくださいませんか。 弊社のGoogleのProfessional-Data-Engineer 学習範囲練習問題を利用したら、あなたは気楽に勉強するだけではなく、順調に試験に合格します。多くの人々はGoogleのProfessional-Data-Engineer 学習範囲試験に合格できるのは難しいことであると思っています。
競争力が激しい社会に当たり、我々Goldmile-Infobizは多くの受験生の中で大人気があるのは受験生の立場からGoogle Professional-Data-Engineer試験概要試験資料をリリースすることです。たとえば、ベストセラーのGoogle Professional-Data-Engineer試験概要問題集は過去のデータを分析して作成ます。ほんとんどお客様は我々Goldmile-InfobizのGoogle Professional-Data-Engineer試験概要問題集を使用してから試験にうまく合格しましたのは弊社の試験資料の有効性と信頼性を説明できます。
Google Professional-Data-Engineer試験概要 - 何の問題があったらお気軽に聞いてください。
Goldmile-Infobizが提供した教育資料は真実のテストに非常に近くて、あなたが弊社の短期の特殊訓練問題を通じてすぐにIT専門の知識を身につけられます。弊社は君の試験の１００％合格率を保証いたします。
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Professional-Data-Engineer PDF DEMO:
QUESTION NO: 1
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: 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 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