弊社のProfessional-Data-Engineer試験内容問題集は大勢の専門家たちの努力で開発される成果です。初心者といい、数年IT仕事を従事した人といい、我々Goldmile-InfobizのGoogle Professional-Data-Engineer試験内容問題集は最良の選択であると考えられます。なぜならば、弊社は高品質かつ改革によってすぐに更新できるProfessional-Data-Engineer試験内容問題集を提供できるからです。 Goldmile-Infobizが提供したGoogleのProfessional-Data-Engineer試験内容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。 短い時間でProfessional-Data-Engineer試験内容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。
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試験内容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 日本語版参考資料問題集の品質を疑わないでください。
私たちはあなたのProfessional-Data-Engineer試験内容試験に関する悩みを解決できます。長い時間で、私たちはProfessional-Data-Engineer試験内容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。
Google Professional-Data-Engineer試験内容 - 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試験内容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試験内容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。
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。IT業界ではさらに強くなるために強い専門知識が必要です。
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