Professional-Data-Engineer受験料過去問 - Google Professional-Data-Engineer日本語資格取得 & Google Certified Professional-Data-Engineer Exam - Royalholidayclubbed

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弊社は強力な教師チームがあって、彼たちは正確ではやくて例年のGoogle Professional-Data-Engineer受験料過去問認定試験の資料を整理して、直ちにもっとも最新の資料を集めて、弊社は全会一緻で認められています。Google Professional-Data-Engineer受験料過去問試験認証に合格確率はとても小さいですが、Royalholidayclubbedはその合格確率を高めることが信じてくだい。その中で、Professional-Data-Engineer受験料過去問認定試験は最も重要な一つです。では、この試験に合格するためにどのように試験の準備をしているのですか。 RoyalholidayclubbedはGoogleのProfessional-Data-Engineer受験料過去問認定試験に向けてもっともよい問題集を研究しています。

Google Cloud Certified Professional-Data-Engineer 早速買いに行きましょう。

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社会と経済の発展につれて、多くの人はIT技術を勉強します。なぜならば、IT職員にとって、GoogleのProfessional-Data-Engineer受験料過去問資格証明書があるのは肝心な指標であると言えます。自分の能力を証明するために、Professional-Data-Engineer受験料過去問試験に合格するのは不可欠なことです。弊社のProfessional-Data-Engineer受験料過去問真題を入手して、試験に合格する可能性が大きくなります。

Royalholidayclubbedの勉強資料を手に入れたら、指示に従えば Professional-Data-Engineer受験料過去問認定試験に受かることはたやすくなります。受験生の皆様にもっと多くの助けを差し上げるために、Royalholidayclubbed のGoogleのProfessional-Data-Engineer受験料過去問トレーニング資料はインターネットであなたの緊張を解消することができます。

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
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: 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 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

QUESTION NO: 5
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

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Updated: May 27, 2022

by RHC.

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