同じ目的を達成するためにいろいろな方法があって、多くの人がいい仕事とすばらしい生活を人生の目的にしています。Omgzlookが提供した研修ツールはGoogleのProfessional-Data-Engineer的中率の認定試験に向けて学習資料やシミュレーション訓練宿題で、重要なのは試験に近い練習問題と解答を提供いたします。Omgzlook を選ばれば短時間にITの知識を身につけることができて、高い点数をとられます。 周りの多くの人は全部Google Professional-Data-Engineer的中率資格認定試験にパースしまして、彼らはどのようにできましたか。今には、あなたにOmgzlookを教えさせていただけませんか。 GoogleのProfessional-Data-Engineer的中率「Google Certified Professional Data Engineer Exam」認証試験に参加者に対して30時間ぐらいの短期の育成訓練でらくらくに勉強しているうちに多くの知識を身につけられます。
Google Cloud Certified Professional-Data-Engineer 最もよくて最新で資料を提供いたします。
GoogleのProfessional-Data-Engineer - Google Certified Professional Data Engineer Exam的中率の試験の資料やほかのトレーニング資料を提供しているサイトがたくさんありますが、GoogleのProfessional-Data-Engineer - Google Certified Professional Data Engineer Exam的中率の認証試験の高品質の資料を提供しているユニークなサイトはOmgzlookです。 Google Professional-Data-Engineer 復習対策「Google Certified Professional Data Engineer Exam」認証試験に合格することが簡単ではなくて、Google Professional-Data-Engineer 復習対策証明書は君にとってはIT業界に入るの一つの手づるになるかもしれません。しかし必ずしも大量の時間とエネルギーで復習しなくて、弊社が丹精にできあがった問題集を使って、試験なんて問題ではありません。
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Google Professional-Data-Engineer的中率 - あなたに絶対向いていると信じていますよ。
Omgzlookは実際の環境で本格的なGoogleのProfessional-Data-Engineer的中率「Google Certified Professional Data Engineer Exam」の試験の準備過程を提供しています。もしあなたは初心者若しくは専門的な技能を高めたかったら、OmgzlookのGoogleのProfessional-Data-Engineer的中率「Google Certified Professional Data Engineer Exam」の試験問題があなたが一歩一歩自分の念願に近くために助けを差し上げます。試験問題と解答に関する質問があるなら、当社は直後に解決方法を差し上げます。しかも、一年間の無料更新サービスを提供します。
でも、受かることが難しいですから、トレーニングツールを利用するのを勧めます。トレーニング資料を選びたいのなら、OmgzlookのGoogleのProfessional-Data-Engineer的中率試験トレーニング資料は最高の選択です。
Professional-Data-Engineer PDF DEMO:
QUESTION NO: 1
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: 2
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: 3
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: 4
You need to create a near real-time inventory dashboard that reads the main inventory tables in your BigQuery data warehouse. Historical inventory data is stored as inventory balances by item and location. You have several thousand updates to inventory every hour. You want to maximize performance of the dashboard and ensure that the data is accurate. What should you do?
A. Use the BigQuery streaming the stream changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
B. Use the BigQuery bulk loader to batch load inventory changes into a daily inventory movement table.
Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
C. Leverage BigQuery UPDATE statements to update the inventory balances as they are changing.
D. Partition the inventory balance table by item to reduce the amount of data scanned with each inventory update.
Answer: C
QUESTION NO: 5
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
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Updated: May 27, 2022