Professional-Data-Engineer専門知識内容問題集のカーバー率が高いので、勉強した問題は試験に出ることが多いです。だから、弊社の提供するProfessional-Data-Engineer専門知識内容問題集を暗記すれば、きっと試験に合格できます。数年以来の整理と分析によって開発されたProfessional-Data-Engineer専門知識内容問題集は権威的で全面的です。 Shobhadoshiの値段よりそれが創造する価値ははるかに大きいです。あなたの予算が限られている場合に完全な問題集を必要としたら、ShobhadoshiのGoogleのProfessional-Data-Engineer専門知識内容試験トレーニング資料を試してみてください。 ShobhadoshiにたくさんのIT専門人士がいって、弊社の問題集に社会のITエリートが認定されて、弊社の問題集は試験の大幅カーバして、合格率が１００％にまで達します。

Google Cloud Certified Professional-Data-Engineer あなたはそれをやったことができましたか。

GoogleのProfessional-Data-Engineer - Google Certified Professional Data Engineer Exam専門知識内容試験に合格することは容易なことではなくて、良い訓練ツールは成功の保証でShobhadoshiは君の試験の問題を準備してしまいました。 ShobhadoshiのGoogleのProfessional-Data-Engineer トレーニング資料試験トレーニング資料の値段は手頃で、IT認証の受験生のみなさんによく適用します。ShobhadoshiのGoogleのProfessional-Data-Engineer トレーニング資料試験資料は同じシラバスに従って研究されたのです。

どんな業界で自分に良い昇進機会があると希望する職人がとても多いと思って、IT業界にも例外ではありません。ITの専門者はGoogleのProfessional-Data-Engineer専門知識内容認定試験があなたの願望を助けって実現できるのがよく分かります。Shobhadoshiはあなたの夢に実現させるサイトでございます。

Google Professional-Data-Engineer専門知識内容 - もうこれ以上悩む必要がないですよ。

Shobhadoshiの専門家チームが君の需要を満たすために自分の経験と知識を利用してGoogleのProfessional-Data-Engineer専門知識内容認定試験対策模擬テスト問題集が研究しました。模擬テスト問題集と真実の試験問題がよく似ています。一目でわかる最新の出題傾向でわかりやすい解説と充実の補充問題があります。

もっと重要なのは、この問題集はあなたが試験に合格することを保証できますから。この問題集よりもっと良いツールは何一つありません。

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
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: 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