GoogleのProfessional-Data-Engineer受験資料更新版試験に関する権威のある学習教材を見つけないで、悩んでいますか？世界中での各地の人々はほとんどGoogleのProfessional-Data-Engineer受験資料更新版試験を受験しています。GoogleのProfessional-Data-Engineer受験資料更新版の認証試験の高品質の資料を提供しているユニークなサイトはGoldmile-Infobizです。もし君はまだ心配することがあったら、私たちのGoogleのProfessional-Data-Engineer受験資料更新版問題集を購入する前に、一部分のフリーな試験問題と解答をダンロードして、試用してみることができます。 Goldmile-InfobizのGoogleのProfessional-Data-Engineer受験資料更新版の認証したカバー率は１００パーセントに達したのですから、弊社の問題と解答を利用する限り、あなたがきっと気楽に試験に合格することを保証します。Goldmile-InfobizのGoogleのProfessional-Data-Engineer受験資料更新版試験のトレーニングキットはGoldmile-InfobizのIT技術専門家たちによって開発されたのです。 もし君はいささかな心配することがあるなら、あなたはうちの商品を購入する前に、Goldmile-Infobizは無料でサンプルを提供することができます。
Google Cloud Certified Professional-Data-Engineer なぜでしょうか。
だから、弊社のProfessional-Data-Engineer - Google Certified Professional Data Engineer Exam受験資料更新版練習資料を早く購入しましょう！ 二つのバージョンのどちらでもダウンロードできますから、Goldmile-Infobizのサイトで検索してダウンロードすることができます。体験してから購入するかどうかを決めてください。
Google Professional-Data-Engineer受験資料更新版認証試験に合格することが簡単ではなくて、Google Professional-Data-Engineer受験資料更新版証明書は君にとってはIT業界に入るの一つの手づるになるかもしれません。しかし必ずしも大量の時間とエネルギーで復習しなくて、弊社が丹精にできあがった問題集を使って、試験なんて問題ではありません。今競争の激しいIT業界で地位を固めたいですが、Google Professional-Data-Engineer受験資料更新版認証試験に合格しなければなりません。
Google Professional-Data-Engineer受験資料更新版 - 人生には様々な選択があります。
GoogleのProfessional-Data-Engineer受験資料更新版試験に趣味があると、躊躇わなく、我々Goldmile-Infobizで問題集のデーモをダウンロードして試すことができます。デーモ版によって、このProfessional-Data-Engineer受験資料更新版問題集はあなたに適合するかと判断します。適合すると、あなたは安心で購買できます。弊社Goldmile-InfobizのProfessional-Data-Engineer受験資料更新版問題集は必ずあなたの成功へ道の秘訣です。
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受験資料更新版問題集の品質を疑わないでください。
Professional-Data-Engineer PDF DEMO:
QUESTION NO: 1
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: 2
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: 3
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: 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
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?
A. Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.
B. Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.
C. Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.
D. Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.
Answer: D
Explanation
It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance, If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud
Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
It is recommended to avoid creating your Compute Engine instance in a different region from your
Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.
Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance
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Updated: May 27, 2022