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Professional-Data-Engineer日本語版対応参考書認定試験もIT領域の幅広い認証を取得しました。
ほんとんどお客様は我々OmgzlookのGoogle Professional-Data-Engineer - Google Certified Professional Data Engineer Exam日本語版対応参考書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。 これは本当に素晴らしいことです。それにもっと大切なのは、Omgzlookのサイトは世界的でProfessional-Data-Engineer 日本語資格取得試験トレーニングによっての試験合格率が一番高いです。
だから、弊社の提供するProfessional-Data-Engineer日本語版対応参考書問題集を暗記すれば、きっと試験に合格できます。数年以来の整理と分析によって開発されたProfessional-Data-Engineer日本語版対応参考書問題集は権威的で全面的です。Professional-Data-Engineer日本語版対応参考書問題集を利用して試験に合格できます。
Google Professional-Data-Engineer日本語版対応参考書 - それと比べるものがありません。
近年、IT領域で競争がますます激しくなります。IT認証は同業種の欠くことができないものになりました。あなたはキャリアで良い昇進のチャンスを持ちたいのなら、OmgzlookのGoogleのProfessional-Data-Engineer日本語版対応参考書「Google Certified Professional Data Engineer Exam」試験トレーニング資料を利用してGoogleの認証の証明書を取ることは良い方法です。現在、GoogleのProfessional-Data-Engineer日本語版対応参考書認定試験に受かりたいIT専門人員がたくさんいます。Omgzlookの試験トレーニング資料はGoogleのProfessional-Data-Engineer日本語版対応参考書認定試験の１００パーセントの合格率を保証します。
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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