Goldmile-Infobizが提供したGoogleのProfessional-Data-Engineer日本語認定対策トレーニング資料を利用する方法です。あなたが試験に合格することにヘルプをあげられますから。それにGoldmile-Infobizは１００パーセント合格率を保証します。 古く時から一寸の光陰軽るんずべからずの諺があって、あなたはどのぐらい時間を無駄にすることができますか？現時点からGoldmile-InfobizのProfessional-Data-Engineer日本語認定対策問題集を学んで、時間を効率的に使用するだけ、Professional-Data-Engineer日本語認定対策知識ポイントを勉強してGoogleのProfessional-Data-Engineer日本語認定対策試験に合格できます。短い時間でProfessional-Data-Engineer日本語認定対策資格認定を取得するような高いハイリターンは嬉しいことではないでしょうか。 もし失敗だったら、我々は全額で返金します。
Google Cloud Certified Professional-Data-Engineer 人生には様々な選択があります。
Professional-Data-Engineer - Google Certified Professional Data Engineer Exam日本語認定対策認定試験の問題集は大勢の人の注目を集め、とても人気がある商品です。 また、Goldmile-Infobizは数え切れない受験生を助け、皆さんの信頼と称賛を得ました。ですから、Goldmile-InfobizのProfessional-Data-Engineer 日本語版対策ガイド問題集の品質を疑わないでください。
長い時間で、私たちはProfessional-Data-Engineer日本語認定対策教材の研究に取り組んでいます。だから、私たちは信頼されるに値します。Professional-Data-Engineer日本語認定対策試験に合格するには、関連する教材を探す必要があります。
Google Professional-Data-Engineer日本語認定対策 - 君の初めての合格を目標にします。
もし君の予算がちょっと不自由で、おまけに質の良いGoogleのProfessional-Data-Engineer日本語認定対策試験トレーニング資料を購入したいなら、Goldmile-InfobizのGoogleのProfessional-Data-Engineer日本語認定対策試験トレーニング資料を選択したほうが良いです。それは値段が安くて、正確性も高くて、わかりやすいです。いろいろな受験生に通用します。あなたはGoldmile-Infobizの学習教材を購入した後、私たち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。
多くの人々は高い難度のIT認証試験に合格するのは専門の知識が必要だと思います。それは確かにそうですが、その知識を身につけることは難しくないとといわれています。
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