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Professional-Data-Engineer受験準備、Google Professional-Data-Engineer全真問題集 & Google Certified Professional-Data-Engineer Exam - Royalholidayclubbed
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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受験準備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受験準備試験に合格するのは不可欠なことです。 弊社は量豊かのIT試験資料を所有するから、あなたは別のGoogle Professional-Data-Engineer受験準備試験に関心を寄せるなら、Royalholidayclubbedでは需要したい資料を尋ねたり、弊社の職員に問い合わせたりしています。安全かつ最も信頼性の高いGoogle Professional-Data-Engineer受験準備問題集販売サイトとして、我々はお客様の個人情報を内緒し、支払いの安全性を保証しています。 あなたにGoogle Professional-Data-Engineer受験準備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 成功を祈ります。
Google Cloud Certified Professional-Data-Engineer受験準備 - Google Certified Professional Data Engineer Exam 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。 GoogleのProfessional-Data-Engineer 無料試験認定試験「Google Certified Professional Data Engineer Exam」によい準備ができて、試験に穏やかな心情をもって扱うことができます。Royalholidayclubbedの専門家が研究された問題集を利用してください。
RoyalholidayclubbedのGoogleのProfessional-Data-Engineer受験準備試験問題資料は質が良くて値段が安い製品です。我々は低い価格と高品質の模擬問題で受験生の皆様に捧げています。我々は心からあなたが首尾よく試験に合格することを願っています。
Google Professional-Data-Engineer受験準備 - 私の夢は最高のIT専門家になることです。
IT業種のGoogleのProfessional-Data-Engineer受験準備認定試験に合格したいのなら、Royalholidayclubbed GoogleのProfessional-Data-Engineer受験準備試験トレーニング問題集を選ぶのは必要なことです。GoogleのProfessional-Data-Engineer受験準備認定試験に受かったら、あなたの仕事はより良い保証を得て、将来のキャリアで、少なくともIT領域であなたの技能と知識は国際的に認知され、受け入れられるです。これも多くの人々がGoogleのProfessional-Data-Engineer受験準備認定試験を選ぶ理由の一つです。その理由でこの試験はますます重視されるになります。Royalholidayclubbed GoogleのProfessional-Data-Engineer受験準備試験トレーニング資料はあなたが上記の念願を実現することを助けられるのです。Royalholidayclubbed GoogleのProfessional-Data-Engineer受験準備試験トレーニング資料は豊富な経験を持っているIT専門家が研究したもので、問題と解答が緊密に結んでいますから、比べるものがないです。高い価格のトレーニング授業を受けることはなくて、Royalholidayclubbed GoogleのProfessional-Data-Engineer受験準備試験トレーニング資料をショッピングカートに入れる限り、我々はあなたが気楽に試験に合格することを助けられます。
きっと望んでいるでしょう。では、常に自分自身をアップグレードする必要があります。
Professional-Data-Engineer PDF DEMO:
QUESTION NO: 1
Which Google Cloud Platform service is an alternative to Hadoop with Hive?
A. Cloud Datastore
B. Cloud Bigtable
C. BigQuery
D. Cloud Dataflow
Answer: C
Explanation
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis.
Google BigQuery is an enterprise data warehouse.
Reference: https://en.wikipedia.org/wiki/Apache_Hive
QUESTION NO: 2
You want to use Google Stackdriver Logging to monitor Google BigQuery usage. You need an instant notification to be sent to your monitoring tool when new data is appended to a certain table using an insert job, but you do not want to receive notifications for other tables. What should you do?
A. Using the Stackdriver API, create a project sink with advanced log filter to export to Pub/Sub, and subscribe to the topic from your monitoring tool.
B. In the Stackdriver logging admin interface, enable a log sink export to Google Cloud Pub/Sub, and subscribe to the topic from your monitoring tool.
C. In the Stackdriver logging admin interface, and enable a log sink export to BigQuery.
D. Make a call to the Stackdriver API to list all logs, and apply an advanced filter.
Answer: C
QUESTION NO: 3
You have an Apache Kafka Cluster on-prem with topics containing web application logs. You need to replicate the data to Google Cloud for analysis in BigQuery and Cloud Storage. The preferred replication method is mirroring to avoid deployment of Kafka Connect plugins.
What should you do?
A. Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Sink connector. Use a Dataflow job to read fron PubSub and write to GCS.
B. Deploy a Kafka cluster on GCE VM Instances. Configure your on-prem cluster to mirror your topics to the cluster running in GCE. Use a Dataproc cluster or Dataflow job to read from Kafka and write to
GCS.
C. Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a
Source connector. Use a Dataflow job to read fron PubSub and write to GCS.
D. Deploy a Kafka cluster on GCE VM Instances with the PubSub Kafka connector configured as a Sink connector. Use a Dataproc cluster or Dataflow job to read from Kafka and write to GCS.
Answer: B
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
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
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Updated: May 27, 2022
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