弊社のOmgzlookはIT認定試験のソフトの一番信頼たるバンドになるという目標を達成するために、弊社はあなたに最新版のGoogleのProfessional-Data-Engineer入門知識試験問題集を提供いたします。弊社のソフトを使用して、ほとんどのお客様は難しいと思われているGoogleのProfessional-Data-Engineer入門知識試験に順調に剛角しました。これも弊社が自信的にあなたに商品を薦める原因です。 OmgzlookはProfessional-Data-Engineer入門知識認定試験に対する短期で有効な訓練を提供するウェブサイト、Professional-Data-Engineer入門知識認定試験が生活の変化をもたらすテストでございます。合格書を持ち方が持たない人により高い給料をもうけられます。 試験に失敗したら、全額で返金する承諾があります。
Professional-Data-Engineer入門知識認定試験もIT領域の幅広い認証を取得しました。
たとえば、ベストセラーのGoogle Professional-Data-Engineer - Google Certified Professional Data Engineer Exam入門知識問題集は過去のデータを分析して作成ます。 それにもっと大切なのは、Omgzlookのサイトは世界的でProfessional-Data-Engineer 参考書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。OmgzlookはGoogleのProfessional-Data-Engineer 参考書認定試験に受かりたい各受験生に明確かつ顕著なソリューションを提供しました。
数年以来の整理と分析によって開発されたProfessional-Data-Engineer入門知識問題集は権威的で全面的です。Professional-Data-Engineer入門知識問題集を利用して試験に合格できます。この問題集の合格率は高いので、多くのお客様からProfessional-Data-Engineer入門知識問題集への好評をもらいました。
Google Professional-Data-Engineer入門知識 - Googleの試験はどうですか。
GoogleのProfessional-Data-Engineer入門知識認定試験に受かるのはあなたの技能を検証することだけでなく、あなたの専門知識を証明できて、上司は無駄にあなたを雇うことはしないことの証明書です。当面、IT業界でGoogleのProfessional-Data-Engineer入門知識認定試験の信頼できるソースが必要です。Omgzlookはとても良い選択で、Professional-Data-Engineer入門知識の試験を最も短い時間に縮められますから、あなたの費用とエネルギーを節約することができます。それに、あなたに美しい未来を作ることに助けを差し上げられます。
それと比べるものがありません。専門的な団体と正確性の高いGoogleのProfessional-Data-Engineer入門知識問題集があるこそ、Omgzlookのサイトは世界的でProfessional-Data-Engineer入門知識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。
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