Professional-Data-Engineer練習問題 & Google Certified Professional-Data-Engineer Exam的中問題集 - Omgzlook

社会と経済の発展につれて、多くの人はIT技術を勉強します。なぜならば、IT職員にとって、GoogleのProfessional-Data-Engineer練習問題資格証明書があるのは肝心な指標であると言えます。自分の能力を証明するために、Professional-Data-Engineer練習問題試験に合格するのは不可欠なことです。 PDF版のProfessional-Data-Engineer練習問題問題集は読みやすくて、忠実に試験の問題を再現することができます。テストエンジンとして、ソフトウェア版のProfessional-Data-Engineer練習問題問題集はあなたの試験の準備についての進捗状況をテストするために利用することができます。 あなたにGoogle Professional-Data-Engineer練習問題試験に関する最新かつ最完備の資料を勉強させ、試験に合格させることだと信じます。

Google Cloud Certified Professional-Data-Engineer こうして、君は安心で試験の準備を行ってください。

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Omgzlookは実際の環境で本格的なGoogleのProfessional-Data-Engineer練習問題「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練習問題「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。

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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

OmgzlookのGoogleのNutanix NCP-DB-6.5試験問題資料は質が良くて値段が安い製品です。 我々社Omgzlookは試験政策の変化に応じて、GoogleのMicrosoft MB-910問題集をタイムリーに更新しています。 Salesforce CPQ-Specialist - 優れたキャリアを持ったら、社会と国のために色々な利益を作ることができて、国の経済が継続的に発展していることを進められるようになります。 弊社の誠意を信じてもらいたいし、Google Microsoft AZ-305試験2成功するのを祈って願います。 ISACA CISA-CN - その夢は私にとってはるか遠いです。

Updated: May 27, 2022