暇の時間を利用して勉強します。努力すれば報われますなので、Google Professional-Data-Engineer最新問題資格認定を取得して自分の生活状況を改善できます。IT職員のあなたは毎月毎月のあまり少ない給料を持っていますが、暇の時間でひたすら楽しむんでいいですか。 では、どうやって、最も早い時間でGoogleのProfessional-Data-Engineer最新問題認定試験に合格するのですか。Omgzlookは君にとって最高な選択になっています。 そして、Professional-Data-Engineer最新問題試験参考書の問題は本当の試験問題とだいたい同じことであるとわかります。
Google Cloud Certified Professional-Data-Engineer きっと君に失望させないと信じています。
しかしも、品質はもっと高くて一度Professional-Data-Engineer - Google Certified Professional Data Engineer Exam最新問題試験に合格したい客様に対して、我が社のProfessional-Data-Engineer - Google Certified Professional Data Engineer Exam最新問題はあなたの最高選択かつ成功のショートカットであると思われます。 我々は受験生の皆様により高いスピードを持っているかつ効率的なサービスを提供することにずっと力を尽くしていますから、あなたが貴重な時間を節約することに助けを差し上げます。Omgzlook GoogleのProfessional-Data-Engineer 資格講座試験問題集はあなたに問題と解答に含まれている大量なテストガイドを提供しています。
我々OmgzlookはGoogleのProfessional-Data-Engineer最新問題試験問題集をリリースする以降、多くのお客様の好評を博したのは弊社にとって、大変な名誉なことです。また、我々はさらに認可を受けられるために、皆様の一切の要求を満足できて喜ぶ気持ちでずっと協力し、完備かつ精確のProfessional-Data-Engineer最新問題試験問題集を開発するのに準備します。
Google Professional-Data-Engineer最新問題 - 素晴らしい試験参考書です。
IT認定試験の中でどんな試験を受けても、OmgzlookのProfessional-Data-Engineer最新問題試験参考資料はあなたに大きなヘルプを与えることができます。それは OmgzlookのProfessional-Data-Engineer最新問題問題集には実際の試験に出題される可能性がある問題をすべて含んでいて、しかもあなたをよりよく問題を理解させるように詳しい解析を与えますから。真剣にOmgzlookのGoogle Professional-Data-Engineer最新問題問題集を勉強する限り、受験したい試験に楽に合格することができるということです。
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最新問題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最新問題試験認証に合格確率はとても小さいですが、Omgzlookはその合格確率を高めることが信じてくだい。
Professional-Data-Engineer PDF DEMO:
QUESTION NO: 1
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: 2
You have Cloud Functions written in Node.js that pull messages from Cloud Pub/Sub and send the data to BigQuery. You observe that the message processing rate on the Pub/Sub topic is orders of magnitude higher than anticipated, but there is no error logged in Stackdriver Log Viewer. What are the two most likely causes of this problem? Choose 2 answers.
A. Publisher throughput quota is too small.
B. The subscriber code cannot keep up with the messages.
C. The subscriber code does not acknowledge the messages that it pulls.
D. Error handling in the subscriber code is not handling run-time errors properly.
E. Total outstanding messages exceed the 10-MB maximum.
Answer: B,D
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
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: 5
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
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Updated: May 27, 2022