ですから、君はうちの学習教材を安心で使って、きみの認定試験に合格することを保証します。多くのサイトの中で、どこかのGoogleのProfessional-Data-Engineerトレーリング学習試験問題集は最も正確性が高いですか。無論Goldmile-InfobizのGoogleのProfessional-Data-Engineerトレーリング学習問題集が一番頼りになります。 人生のチャンスを掴むことができる人は殆ど成功している人です。ですから、ぜひGoldmile-Infobizというチャンスを掴んでください。 いろいろな受験生に通用します。
Google Cloud Certified Professional-Data-Engineer では、どんな参考書は選べる価値を持っていますか。
Google Cloud Certified Professional-Data-Engineerトレーリング学習 - Google Certified Professional Data Engineer Exam この時代にはIT資格認証を取得するは重要になります。 もしうちの学習教材を購入するなら、Goldmile-Infobizは一年間で無料更新サービスを提供することができます。Goldmile-InfobizのGoogleのProfessional-Data-Engineer 資格トレーリング認定試験の合格率は１００パーセントになっています。
我々社はGoogle Professional-Data-Engineerトレーリング学習問題集をリリースされる以来、たくさんの好評を博しました。試験に合格したお客様は「Professional-Data-Engineerトレーリング学習問題集のオンライン版を利用して、模擬試験を繰り返して受けました。無事試験に合格しました。
Google Professional-Data-Engineerトレーリング学習 - 弊社の商品はあなたの圧力を減少できます。
Professional-Data-Engineerトレーリング学習試験は難しいです。だから、Professional-Data-Engineerトレーリング学習復習教材を買いました。本当に助かりました。先月、Professional-Data-Engineerトレーリング学習試験に参加しました。今日は、試験の結果をチエックし、嬉しいことに、Professional-Data-Engineerトレーリング学習試験に合格しました。Professional-Data-Engineerトレーリング学習復習教材は有効的な資料です。
だから、こんなに保障がある復習ソフトはあなたにGoogleのProfessional-Data-Engineerトレーリング学習試験を心配させていません。我々Goldmile-InfobizのGoogleのProfessional-Data-Engineerトレーリング学習試験のソフトウェアを使用し、あなたはGoogleのProfessional-Data-Engineerトレーリング学習試験に合格することができます。
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