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。あなたに便利なオンラインサービスを提供して、Google Professional-Data-Engineer学習指導試験問題についての全ての質問を解決して差し上げます。OmgzlookのGoogleのProfessional-Data-Engineer学習指導試験問題資料は質が良くて値段が安い製品です。 長年の努力を通じて、OmgzlookのGoogleのProfessional-Data-Engineer学習指導認定試験の合格率が１００パーセントになっていました。Omgzlookを選ぶのは、成功を選ぶのに等しいと言えます。 GoogleのProfessional-Data-Engineer学習指導認定試験は実は技術専門家を認証する試験です。
Google Cloud Certified Professional-Data-Engineer それはあなたが夢を実現することを助けられます。
我々の開発するGoogleのProfessional-Data-Engineer - Google Certified Professional Data Engineer Exam学習指導ソフトは最新で最も豊富な問題集を含めています。 さて、はやく試験を申し込みましょう。Omgzlookはあなたを助けることができますから、心配する必要がないですよ。
我々はほぼ100％の通過率であなたに安心させます。すべての売主は試験に失敗したら全額で返金するのを承諾できるわけではない。我々OmgzlookのITエリートと我々のGoogleのProfessional-Data-Engineer学習指導試験のソフトに満足するお客様は我々に自信を持たせます。
Google Professional-Data-Engineer学習指導 - Omgzlookから大変助かりました。
OmgzlookのGoogleのProfessional-Data-Engineer学習指導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す。君がGoogleのProfessional-Data-Engineer学習指導問題集を購入したら、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。もしGoogleのProfessional-Data-Engineer学習指導問題集は問題があれば、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。
70％の問題は解説がありますし、試験の内容を理解しやすいと助けます。常にGoogle Professional-Data-Engineer学習指導試験に参加する予定があるお客様は「こちらの問題集には、全部で何問位、掲載されておりますか？」といった質問を提出しました。
Professional-Data-Engineer PDF DEMO:
QUESTION NO: 1
You are building a model to make clothing recommendations. You know a user's fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available.
How should you use this data to train the model?
A. Continuously retrain the model on just the new data.
B. Continuously retrain the model on a combination of existing data and the new data.
C. Train on the new data while using the existing data as your test set.
D. Train on the existing data while using the new data as your test set.
Answer: C
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
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
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: 5
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
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Updated: May 27, 2022