IT국제공인자격증Google Professional-Data-Engineer Pdf시험대비덤프를 제공하는 전문적인 사이트로서 회원님의 개인정보를 철저하게 보호해드리고 페이팔을 통한 결제라 안전한 결제를 진행할수 있습니다. Google Professional-Data-Engineer Pdf 덤프외에 다른 인증시험덤프에 관심이 있으신 분은 온라인 서비스를 클릭하여 문의해주세요. 희망찬 내일을 위하여 Shobhadoshi선택은 정답입니다. Shobhadoshi선택함으로 당신이 바로 진정한IT인사입니다. 다같이 Google Professional-Data-Engineer Pdf덤프로 시험패스에 주문걸어 보아요.

Google Cloud Certified Professional-Data-Engineer 만약 아직도 우리를 선택할지에 대하여 망설이고 있다면.

Google Cloud Certified Professional-Data-Engineer Pdf - Google Certified Professional Data Engineer Exam IT인증시험을Shobhadoshi덤프로 준비해야만 하는 이유는Shobhadoshi덤프는 IT업계전문가들이 실제시험문제를 연구하여 시험문제에 대비하여 예상문제를 제작했다는 점에 있습니다. 여러분이 다른 사이트에서도Google인증Professional-Data-Engineer 최신기출자료시험 관련덤프자료를 보셨을 것입니다 하지만 우리Shobhadoshi의 자료만의 최고의 전문가들이 만들어낸 제일 전면적이고 또 최신 업데이트일 것입니다.Google인증Professional-Data-Engineer 최신기출자료시험을 응시하고 싶으시다면 Shobhadoshi자료만의 최고의 선택입니다. Google인증Professional-Data-Engineer 최신기출자료시험덤프의 문제와 답은 모두 우리의 엘리트들이 자신의 지식과 몇 년간의 경험으로 완벽하게 만들어낸 최고의 문제집입니다.

Google Professional-Data-Engineer Pdf인증시험을 어떻게 준비하면 될가 아직도 고민하고 계시죠? 학원에 등록하자니 시간도 없고 돈도 많이 들고 쉽게 엄두가 나지 않는거죠? Shobhadoshi제품을 구매하신다면 그런 부담을 이제 끝입니다. Shobhadoshi덤프는 더욱 가까지 여러분들께 다가가기 위하여 그 어느 덤프판매 사이트보다 더욱 저렴한 가격으로 여러분들을 맞이하고 있습니다. Google Professional-Data-Engineer Pdf덤프는Shobhadoshi제품이 최고랍니다.

Google Professional-Data-Engineer Pdf - 두 버전을 모두 구입하시면 시험에서 고득점으로 패스가능합니다.

Shobhadoshi의 Google인증 Professional-Data-Engineer Pdf덤프를 구매하시고 공부하시면 밝은 미래를 예약한것과 같습니다. Shobhadoshi의 Google인증 Professional-Data-Engineer Pdf덤프는 고객님이 시험에서 통과하여 중요한 IT인증자격증을 취득하게끔 도와드립니다. IT인증자격증은 국제적으로 인정받기에 취직이나 승진 혹은 이직에 힘을 가해드립니다. 학원공부나 다른 시험자료가 필요없이Shobhadoshi의 Google인증 Professional-Data-Engineer Pdf덤프만 공부하시면Google인증 Professional-Data-Engineer Pdf시험을 패스하여 자격증을 취득할수 있습니다.

Shobhadoshi의Google인증 Professional-Data-Engineer Pdf시험대비 덤프는 가격이 착한데 비하면 품질이 너무 좋은 시험전 공부자료입니다. 시험문제적중율이 높아 패스율이 100%에 이르고 있습니다.다른 IT자격증에 관심이 있는 분들은 온라인서비스에 문의하여 덤프유무와 적중율등을 확인할수 있습니다.

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
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: 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

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Updated: May 27, 2022