Professional-Data-Engineer基礎訓練、Google Professional-Data-Engineer練習問題 & Google Certified Professional-Data-Engineer Exam - Royalholidayclubbed

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Google Cloud Certified Professional-Data-Engineer 我々の誠意を信じてください。

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Google Professional-Data-Engineer基礎訓練 - 我々のデモから感じられます。

現在IT技術会社に通勤しているあなたは、GoogleのProfessional-Data-Engineer基礎訓練試験認定を取得しましたか？Professional-Data-Engineer基礎訓練試験認定は給料の増加とジョブのプロモーションに役立ちます。短時間でProfessional-Data-Engineer基礎訓練試験に一発合格したいなら、我々社のGoogleのProfessional-Data-Engineer基礎訓練資料を参考しましょう。また、Professional-Data-Engineer基礎訓練問題集に疑問があると、メールで問い合わせてください。

GoogleのProfessional-Data-Engineer基礎訓練試験にリラクスで合格するのも可能性があります。我々Royalholidayclubbedの提供するGoogleのProfessional-Data-Engineer基礎訓練試験のソフトを利用した多くのお客様はこのような感じがあります。

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

だから、我々社は力の限りで弊社のGoogle　CompTIA XK0-005試験資料を改善し、改革の変更に応じて更新します。我々提供するGoogleのUiPath UiPath-ADPv1ソフトであなたはきっと試験に合格できます。あなたはAmazon SAA-C03-KR試験に不安を持っていますか？Amazon SAA-C03-KR参考資料をご覧下さい。 Fortinet FCP_ZCS_AD-7.4 - あなたのすべての需要を満たすためには、一緒に努力します。 Royalholidayclubbedは専門のIT業界での評判が高くて、あなたがインターネットでRoyalholidayclubbedの部分のGoogle PCI SSC QSA_New_V4「Google Certified Professional Data Engineer Exam」資料を無料でダウンロードして、弊社の正確率を確認してください。

Updated: May 27, 2022

by RHC.

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