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IT업계에 종사하고 계시나요? 최근 유행하는Google인증 Professional-Data-Engineer IT인증시험에 도전해볼 생각은 없으신지요? IT 인증자격증 취득 의향이 있으시면 저희. Itexamdump의 Google인증 Professional-Data-Engineer덤프로 시험을 준비하시면 100%시험통과 가능합니다. Itexamdump의 Google인증 Professional-Data-Engineer덤프는 착한 가격에 고품질을 지닌 최고,최신의 버전입니다. Itexamdump덤프로 가볼가요?
Google Professional-Data-Engineer 시험에 응시하려면, 후두(Hadoop), 스파크(Spark) 및 기타 빅데이터 프레임워크와 같은 데이터 처리 기술에 대한 깊은 이해가 필요합니다. 또한, Python, Java 또는 Go와 같은 프로그래밍 언어에 능숙해야 하며, 데이터 처리 파이프라인을 설계하고 개발하는 경험이 있어야 합니다. 게다가, 후보자들은 BigQuery, Dataflow 및 Dataproc과 같은 Google Cloud Platform 서비스를 직접 사용해본 경험이 있어야 합니다. Google Professional-Data-Engineer 시험에 합격하면, Google Cloud에서 데이터 솔루션을 관리하거나 전문성을 입증하려는 데이터 전문가들에게 유용한 자산이 될 수 있습니다.
>> Professional-Data-Engineer인증시험 인기 덤프문제 <<
학원다니면서 많은 지식을 장악한후Google Professional-Data-Engineer시험보시는것도 좋지만 회사다니느랴 야근하랴 시간이 부족한 분들은Google Professional-Data-Engineer덤프만 있으면 엄청난 학원수강료 필요없이 20~30시간의 독학만으로도Google Professional-Data-Engineer시험패스가 충분합니다. 또한 취업생분들은 우선 자격증으로 취업문을 두드리고 일하면서 실무를 익혀가는방법도 좋지 않을가 생각됩니다.
Google Professional-Data-Engineer 자격증은 산업에서 높은 가치를 두고 있습니다. 이 자격증은 소지자가 Google Cloud Platform에서 데이터 솔루션을 설계하고 구현하는 데 필요한 기술과 지식을 갖추었음을 나타냅니다. 이 자격증은 특히 빅 데이터를 다루려는 사람들에게 유용하며, Google Cloud Platform은 빅 데이터 솔루션의 주요 제공 업체 중 하나입니다.
Google Professional-Data-Engineer 자격증 시험은 데이터 엔지니어링, 데이터 분석, 기계 학습 및 대용량 데이터 처리와 같은 다양한 분야의 지식과 기술을 검증하는 온라인 시험입니다. 이 시험은 객관식 문항으로 구성되어 있으며, Google Cloud Platform, 그 서비스 및 기능에 대한 철저한 이해력이 필요합니다. 후보자는 비즈니스 요구 사항을 충족하는 데이터 처리 시스템을 설계하고 구현하는 능력을 증명해야합니다.
질문 # 14
You have a job that you want to cancel. It is a streaming pipeline, and you want to ensure that any data that is in-flight is processed and written to the output. Which of the following commands can you use on the Dataflow monitoring console to stop the pipeline job?
정답：B
설명：
Using the Drain option to stop your job tells the Dataflow service to finish your job in its current state. Your job will immediately stop ingesting new data from input sources, but the Dataflow service will preserve any existing resources (such as worker instances) to finish processing and writing any buffered data in your pipeline.
Reference: https://cloud.google.com/dataflow/pipelines/stopping-a-pipeline
질문 # 15
Cloud Dataproc is a managed Apache Hadoop and Apache _____ service.
정답：B
설명：
Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.
질문 # 16
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?
정답：B
질문 # 17
You have several Spark jobs that run on a Cloud Dataproc cluster on a schedule. Some of the jobs run in sequence, and some of the jobs run concurrently. You need to automate this process. What should you do?
정답：D
질문 # 18
MJTelco Case Study
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the
world. The company has patents for innovative optical communications hardware. Based on these patents,
they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to
overcome communications challenges in space. Fundamental to their operation, they need to create a
distributed data infrastructure that drives real-time analysis and incorporates machine learning to
continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the
network allowing them to account for the impact of dynamic regional politics on location availability and
cost.
Their management and operations teams are situated all around the globe creating many-to-many
relationship between data consumers and provides in their system. After careful consideration, they
decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
Scale and harden their PoC to support significantly more data flows generated when they ramp to more
than 50,000 installations.
Refine their machine-learning cycles to verify and improve the dynamic models they use to control
topology definition.
MJTelco will also use three separate operating environments - development/test, staging, and production
- to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
Scale up their production environment with minimal cost, instantiating resources when and where
needed in an unpredictable, distributed telecom user community.
Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
Provide reliable and timely access to data for analysis from distributed research workers
Maintain isolated environments that support rapid iteration of their machine-learning models without
affecting their customers.
Technical Requirements
Ensure secure and efficient transport and storage of telemetry data
Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows
each.
Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately
100m records/day
Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems
both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive
hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize
our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data
secure. We also need environments in which our data scientists can carefully study and quickly adapt our
models. Because we rely on automation to process our data, we also need our development and test
environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis.
Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on
automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to
work on our high-value problems instead of problems with our data pipelines.
MJTelco's Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000
installations. You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud
Dataflow pipeline configuration setting should you update?
정답：D
질문 # 19
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