Professional-Data-Engineer関連合格問題 & Professional-Data-Engineer認定資格試験 - Google Professional-Data-Engineer模擬試験問題集 - Omgzlook

OmgzlookはGoogleのProfessional-Data-Engineer関連合格問題認定試験に向けてもっともよい問題集を研究しています。もしほかのホームページに弊社みたいな問題集を見れば、あとでみ続けて、弊社の商品を盗作することとよくわかります。Omgzlookが提供した資料は最も全面的で、しかも更新の最も速いです。 OmgzlookのGoogleのProfessional-Data-Engineer関連合格問題試験トレーニング資料を使ったら、君のGoogleのProfessional-Data-Engineer関連合格問題認定試験に合格するという夢が叶えます。なぜなら、それはGoogleのProfessional-Data-Engineer関連合格問題認定試験に関する必要なものを含まれるからです。 Omgzlookのシニア専門家チームはGoogleのProfessional-Data-Engineer関連合格問題試験に対してトレーニング教材を研究できました。

Google Cloud Certified Professional-Data-Engineer Omgzlookを選択したら、成功をとりましょう。

自分の能力を証明するために、Professional-Data-Engineer - Google Certified Professional Data Engineer Exam関連合格問題試験に合格するのは不可欠なことです。 Omgzlookの勉強資料を手に入れたら、指示に従えば Professional-Data-Engineer 日本語対策問題集認定試験に受かることはたやすくなります。受験生の皆様にもっと多くの助けを差し上げるために、Omgzlook のGoogleのProfessional-Data-Engineer 日本語対策問題集トレーニング資料はインターネットであなたの緊張を解消することができます。

我々Omgzlookは一番行き届いたアフタサービスを提供します。Google Professional-Data-Engineer関連合格問題試験問題集を購買してから、一年間の無料更新を楽しみにしています。あなたにGoogle Professional-Data-Engineer関連合格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 Professional-Data-Engineer関連合格問題 - 心配する必要はないです。

Omgzlookは実際の環境で本格的なGoogleのProfessional-Data-Engineer関連合格問題「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関連合格問題「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。

がむしゃらに試験に要求された関連知識を積み込むより、価値がある問題を勉強したほうがいいです。効率のあがる試験問題集は受験生の皆さんにとって欠くことができないツールです。

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
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

QUESTION NO: 3
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: 4
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: 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

ISACA CISA-JPN - 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のMicrosoft MB-280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Microsoft MB-280試験を受験しています。 OmgzlookのGoogleのUSGBC LEED-AP-ND試験トレーニング資料はIT人員の皆さんがそんな目標を達成できるようにヘルプを提供して差し上げます。 もしOmgzlookのGoogleのSAP C_THR88_2405問題集を購入したら、学習教材はどんな問題があれば、或いは試験に不合格になる場合は、全額返金することを保証いたします。 OmgzlookのGoogleのNAHQ CPHQ試験トレーニング資料を利用して気楽に試験に合格しました。

Updated: May 27, 2022