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弊社の試験問題はほとんど毎月で一回アップデートしますから、あなたは市場で一番新鮮な、しかも依頼できる良い資源を得ることができることを保証いたします。Royalholidayclubbedは当面最新のGoogleのProfessional-Data-Engineer復習内容の認証試験の準備問題を提供している認証された候補者のリーダーです。弊社の資源はずっと改訂され、アップデートされていますから、緊密な相関関係があります。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、RoyalholidayclubbedのGoogleのProfessional-Data-Engineer復習内容「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復習内容認定試験に受かりたいIT専門人員がたくさんいます。それは我々はGoogleのProfessional-Data-Engineer復習内容問題集やProfessional-Data-Engineer復習内容スタディガイドやProfessional-Data-Engineer復習内容問題と解答がたくさんありますから。

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。

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あなたはProfessional-Data-Engineer復習内容試験に不安を持っていますか？Professional-Data-Engineer復習内容参考資料をご覧下さい。私たちのProfessional-Data-Engineer復習内容参考資料は十年以上にわたり、専門家が何度も練習して、作られました。あなたに高品質で、全面的なProfessional-Data-Engineer復習内容参考資料を提供することは私たちの責任です。私たちより、Professional-Data-Engineer復習内容試験を知る人はいません。

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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
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: 3
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: 4
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: 5
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

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Updated: May 27, 2022

by RHC.

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