Professional-Data-Engineer테스트자료 & Google Professional-Data-Engineer덤프공부문제 - Google Certified Professional-Data-Engineer Exam - Omgzlook

Google Professional-Data-Engineer테스트자료 시험 기출문제를 애타게 찾고 계시나요? Omgzlook의 Google Professional-Data-Engineer테스트자료덤프는Google Professional-Data-Engineer테스트자료최신 시험의 기출문제뿐만아니라 정답도 표기되어 있고 저희 전문가들의 예상문제도 포함되어있어 한방에 응시자분들의 고민을 해결해드립니다. 구매후 시험문제가 변경되면 덤프도 시험문제변경에 따라 업데이트하여 무료로 제공해드립니다. Google인증 Professional-Data-Engineer테스트자료덤프구매후 업데이트될시 업데이트버전을 무료서비스료 제공해드립니다. Google인증 Professional-Data-Engineer테스트자료시험은 인기있는 IT자격증을 취득하는데 필요한 국제적으로 인정받는 시험과목입니다. Omgzlook의 자료만의 제일 전면적이고 또 최신 업데이트일것입니다.

Google Cloud Certified Professional-Data-Engineer Omgzlook에서는 IT인증시험에 대비한 모든 덤프자료를 제공해드립니다.

Google Cloud Certified Professional-Data-Engineer테스트자료 - Google Certified Professional Data Engineer Exam Omgzlook로 여러분은 소유하고 싶은 인증서를 빠른 시일내에 얻게 될것입니다. 성공을 위해 길을 찾고 실패를 위해 구실을 찾지 않는다는 말이 있습니다. Google인증 Professional-Data-Engineer IT인증시험시험이 영어로 출제되어 시험패스가 너무 어렵다 혹은 회사다니느라 공부할 시간이 없다는 등등은 모두 공부하기싫은 구실에 불과합니다.

Omgzlook에서 출시한 Google인증 Professional-Data-Engineer테스트자료덤프는 실제시험문제 커버율이 높아 시험패스율이 가장 높습니다. Google인증 Professional-Data-Engineer테스트자료시험을 통과하여 자격증을 취득하면 여러방면에서 도움이 됩니다. Omgzlook에서 출시한 Google인증 Professional-Data-Engineer테스트자료덤프를 구매하여Google인증 Professional-Data-Engineer테스트자료시험을 완벽하게 준비하지 않으실래요? Omgzlook의 실력을 증명해드릴게요.

Google Google Professional-Data-Engineer테스트자료 덤프의 pdf버전은 인쇄 가능한 버전이라 공부하기도 편합니다.

Professional-Data-Engineer테스트자료는Google의 인증시험입니다.Professional-Data-Engineer테스트자료인증시험을 패스하면Google인증과 한 발작 더 내디딘 것입니다. 때문에Professional-Data-Engineer테스트자료시험의 인기는 날마다 더해갑니다.Professional-Data-Engineer테스트자료시험에 응시하는 분들도 날마다 더 많아지고 있습니다. 하지만Professional-Data-Engineer테스트자료시험의 통과 율은 아주 낮습니다.Professional-Data-Engineer테스트자료인증시험준비중인 여러분은 어떤 자료를 준비하였나요?

Demo를 다운받아Google Professional-Data-Engineer테스트자료덤프의 일부분 문제를 체험해보시고 구매하셔도 됩니다. 저희 Omgzlook에서는Google Professional-Data-Engineer테스트자료덤프의 일부분 문제를 샘플로 제공해드립니다.

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