GCP-DE인증시험을 패스하여 자격증을 취득하는데 가장 쉬운 방법은 덤프를 공부하는 것입니다.GCP-DE 인증덤프는 실제 시험의 가장 최근 시험문제를 기준으로 하여 만들어진 최고품질 , 최고적중율 자료입니다. 덤프에 있는 문제와 답만 기억하시면 시험을 패스하는데 많은 도움이 됩니다.GCP-DE덤프를 선택해야 하는 이유:

DOWNLOAD DEMO

가장 간편하게 공부가능

GCP-DE덤프는 시험예상문제와 기출문제로 되어있는데 실제 시험문제의 모든 유형을 포함하고 있어 덤프에 있는 문제와 답만 기억하시면 GCP-DE시험을 패스할수 있습니다.학원다닐 필요가 없기에 돈과 시간을 절약해드리고 가장 효율적으로 시험을 합격할수 있도록 도와드립니다.GCP-DE덤프제작팀의 부단한 노력으로 인하여 인증시험 패스는 더는 어려운 일이 아닙니다.

100% 시험통과율

구매하시면 구매일로부터 1년내에 GCP-DE덤프가 업데이트된다면 업데이트된 버전을 무료로 제공해드립니다.만약 GCP-DE덤프를 구매하시고 공부한후 시험에서 떨어지면 60일내 주문은 덤프비용 전액을 환불해드려 고객님의 이익을 최대한 보장해드립니다.환불신청은 주문번호와 불합격 성적표만 메일로 보내오시면 됩니다.

유효한 시험자료로 시간절약 가능

GCP-DE시험덤프는 PDF버전, 온라인버전,테스트엔진버전 등 세가지 버전으로 되어있습니다.GCP-DE덤프 PDF버전은 인쇄가능한 버전이라 출력하셔서 공부하시면 좋습니다. 온라인버전은 APP버전인데 휴대폰으로 사용가능합니다. 출근길이나 퇴근길에서도 쉽게 공부하실수 있습니다. 테스트엔진버전은 PC에서 사용가능한 버전입니다. GCP-DE덤프의 원하시는 버전을 단독으로 구매하셔도 되고 패키지로 구매하셔도 됩니다.

최신 Google Cloud Certified GCP-DE 무료샘플문제:

1. You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

A) Create a cron schedule in Cloud Dataprep.
B) Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
C) Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
D) Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.

2. You architect a system to analyze seismic dat
a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

A) Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
B) Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.
C) Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
D) Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.

3. You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

A) Cloud Bigtable
B) Cloud SQL
C) Cloud Datastore
D) BigQuery

4. You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?

A) Use Cloud Vision AutoML, but reduce your dataset twice.
B) Train your own image recognition model leveraging transfer learning techniques.
C) Use Cloud Vision API by providing custom labels as recognition hints.
D) Use Cloud Vision AutoML with the existing dataset.

5. Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

A) Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
B) Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
C) Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
D) Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric

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