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NEW QUESTION: 1
You have an established Layer 3 VPN where you are using static routing between your PE and CE devices. You must advertise their directly connected subnet to remote PE devices.
Which two methods must you use? (Choose two.)
A. Use a route distinguisher.
B. Use the VRF table label.
C. Use a VRF export policy.
D. Use a site identifier.
Answer: B,C

NEW QUESTION: 2
모든 상점의 POS 데이터를 하루 4 회 수집하는 소매 체인을 위해 일하면서 판매 통계를 얻고 상점의 업무량에 따라 인력 스케줄을 계획합니다. 각 상점은 독립적으로 실행되므로 하루 동안 다른 빈도로 제공되는 구조 및 형식의 측면에서 다른 데이터를 가질 수 있습니다. 데이터의 예상 크기는 일반적으로 작지만 매장이 얼마나 바쁜지에 따라 속도가 빠를 수 있습니다. ETL 및 처리는 데이터가 수신 될 때 이벤트 중심으로 수행되어야합니다. 데이터는 여러 변환을 통해 처리되어야하고 복잡한 쿼리를 실행할 수있는 데이터베이스에 저장해야합니다. 어떤 옵션이 최고의 솔루션이 될까요? 특히 인프라 스트럭처를 관리하는 사람들로 구성된 팀과 제한된 예산이 없다는 것을 알고 있습니까? 정답을 선택하십시오.
A. 키네시스 스트림을 사용하여 매장에서 데이터를 가져 와서 탄력적 인 검색을 하십시오.
B. 상점에서 S3으로 데이터를 전송하고, 람다를 사용하여 데이터를 유효성을 검사하고 일괄 적으로 RDS에로드하여 분석하고, 영업 시간 후에 RDS 클러스터를 중지합니다.
C. 데이터를 DynamoDB에 기록하고 필요한 데이터에 대한 스캔을 실행 한 다음 결과를 S3로 덤프하고 QuickSight에서 분석합니다.
D. 상점에서 S3로 데이터를 전송하고 람다를 사용하여 데이터를 유효성을 검사하고 일괄 적으로 EMR / Spark 클러스터에로드 한 다음 분석을 위해 Redshift로 출력을로드하고 업무 시간 후에 EMR 클러스터를 끄십시오
Answer: D
Explanation:
설명:
람다 (Lambda), 비용에 대한 일시적인 EMR 및 분석을위한 Redshift를 사용하는 이벤트 주도

NEW QUESTION: 3
Case Study: 2 - MJTelco
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?
A. The maximum number of workers
B. The disk size per worker
C. The number of workers
D. The zone
Answer: D

NEW QUESTION: 4
You manage the Dynamics 365 environment for Contoso, Ltd. A rule automatically creates a lead associated with an email when an email is sent to [email protected].
You need to ensure that the marketing manager receives an email each time an email request is sent to [email protected].
How should you configure the rule? To answer, drag the appropriate actions to the correct requirements. Each action may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation