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NEW QUESTION: 1
회사는 약 20 바이트 크기 (예측 당 20 기가 바이트)의 10 억 개의 고유 한 위치의 출력 해상도로 15 분마다 업데이트되는 일기 예보를 생성하는 응용 프로그램을 보유하고 있습니다. 1 시간마다 예측 데이터는 전 세계적으로 약 5 백만 번 (초당 1,400 건의 요청) 및 최대 10 배까지 액세스됩니다. 예측 데이터는 업데이트 할 때마다 덮어 씁니다. 현재 일기 예보 응용 프로그램 사용자는 각 요청에 대해 2 초 이내에 쿼리에 대한 응답이 반환 될 것으로 예상합니다.
어떤 디자인이 필요한 요청 속도와 응답 시간을 충족합니까?
A. Amazon ES 클러스터에 예측 위치를 저장합니다. 쿼리에 원본으로 응답하는 AWS Lambda 함수와 함께 Amazon API Gateway 엔드 포인트를 대상으로 하는 Amazon CloudFront 배포를 사용하십시오.
캐시 제어 시간 종료가 15 분으로 설정된 상태에서 API 게이트웨이 스테이지에서 API 캐싱을 사용하십시오.
B. Amazon EFS 볼륨에 예측 위치를 저장합니다. Amazon EFS 볼륨을 마운트 한 Amazon EC2 인스턴스의 Auto Scaling 집합의 Elastic Load Balancing 그룹을 대상으로 하는 Amazon CloudFront 배포를 생성합니다. CloudFront 배포에서 설정된 캐시 제어 시간 초과를 15 분 동안 설정하십시오.
C. Amazon ES 클러스터에 예측 위치를 저장합니다. 쿼리에 원본으로 응답하는 AWS Lambda 함수가 포함 된 API Gateway 엔드 포인트를 대상으로하는 Amazon CloudFront 배포를 사용하십시오. 데이터를 엣지 로케이션에서 15 분 동안 로컬로 캐시하는 Amazon Lambda @ Edge 함수를 생성하십시오.
D. Amazon S3의 예측 위치를 개별 객체로 저장합니다. EC3 인스턴스의 Auto Scaling 집합의 Elastic Load Balancing 그룹을 대상으로 Amazon CloudFront 배포를 생성하고 S3 객체의 출처를 쿼리합니다. CloudFront 배포에서 캐시 제어 시간 초과를 15 분 동안 설정하십시오.
Answer: C
Explanation:
Explanation
https://aws.amazon.com/blogs/networking-and-content-delivery/lambdaedge-design-best-practices/

NEW QUESTION: 2
各ツールをそのタスクに一致させます。
注：正しい一致はそれぞれ1ポイントの価値があります。

Answer:
Explanation:

Explanation

NEW QUESTION: 3
The fingerprint feature that the Anti-DDos packet consistency check function can recognize is
A. abbaabba
B. abababab
C. aaaaaaaa
D. aaaabbbb
Answer: C

NEW QUESTION: 4

A. Option B
B. Option D
C. Option A
D. Option C
Answer: D
Explanation:
Explanation
* Scenario: Predictive Routing:
/ An Azure solution must be used for prediction systems.
/ Predictive analytics must be published as a web service and accessible by using the REST API.
* Microsoft Azure Machine Learning Studio is a collaborative visual development environment that enables you to build, test, and deploy predictive analytics solutions that operate on your data. The Machine Learning service and development environment is cloud-based, provides compute resource and memory flexibility, and eliminates setup and installation concerns because you work through your web browser.
References: https://azure.microsoft.com/en-us/documentation/articles/machine-learning-what-is-ml-studio/
Topic 6, Lucerne PublishingBackground
Overview
Lucerne Publishing creates, stores, and delivers online media for advertising companies. This media is streamed to computers by using the web, and to mobile devices around the world by using native applications.
The company currently supports the iOS, Android, and Windows Phone 8.1 platform.
Lucerne Publishing uses proprietary software to manage its media workflow. This software has reached the end of its lifecycle. The company plans to move its media workflows to the cloud. Lucerne Publishing provides access to its customers, who are third-party companies, so that they can download, upload, search, and index media that is stored on Lucerne Publishing servers.
Apps and Applications
Lucerne Publishing develops the applications that customers use to deliver media. The company currently provides the following media delivery applications:
* Lucerne Media W - a web application that delivers media by using any browser
* Lucerne Media M - a mobile app that delivers media by using Windows Phone 8.1
* Lucerne Media A - a mobile app that delivers media by using an iOS device
* Lucerne Media N - a mobile app that delivers media by using an Android device
* Lucerne Media D - a desktop client application that customer's install on their local computer Business Requirements Lucerne Publishing's customers and their consumers have the following requirements:
* Access to media must be time-constricted once media is delivered to a consumer.
* The time required to download media to mobile devices must be minimized.
* Customers must have 24-hour access to media downloads regardless of their location or time zone.
* Lucerne Publishing must be able to monitor the performance and usage of its customer-facing app.
Lucerne Publishing wants to make its asset catalog searchable without requiring a database redesign.
* Customers must be able to access all data by using a web application. They must also be able to access data by using a mobile app that is provided by Lucerne Publishing.
* Customers must be able to search for media assets by key words and media type.
* Lucerne Publishing wants to move the asset catalog database to the cloud without formatting the source data.
Other Requirements
Development
Code and current development documents must be backed up at all times. All solutions must be automatically built and deployed to Azure when code is checked in to source control.
Network Optimization
Lucerne Publishing has a .NET web application that runs on Azure. The web application analyzes storage and the distribution of its media assets. It needs to monitor the utilization of the web application. Ultimately, Lucerne Publishing hopes to cut its costs by reducing data replication without sacrificing its quality of service to its customers. The solution has the following requirements:
* Optimize the storage location and amount of duplication of media.
* Vary several parameters including the number of data nodes and the distance from node to customers.
* Minimize network bandwidth.
* Lucerne Publishing wants be notified of exceptions in the web application.
Technical Requirements
Data Mining
Lucerne Publishing constantly mines its data to identify customer patterns. The company plans to replace the existing on-premises cluster with a cloud-based solution. Lucerne Publishing has the following requirements:
Virtual machines:
* The data mining solution must support the use of hundreds to thousands of processing cores.
* Minimize the number of virtual machines by using more powerful virtual machines. Each virtual machine must always have eight or more processor cores available.
* Allow the number of processor cores dedicated to an analysis to grow and shrink automatically based on the demand of the analysis.
* Virtual machines must use remote memory direct access to improve performance.
Task scheduling:
The solution must automatically schedule jobs. The scheduler must distribute the jobs based on the demand and available resources.
Data analysis results:
The solution must provide a web service that allows applications to access the results of analyses.
Other Requirements
Feature Support
* Ad copy data must be searchable in full text.
* Ad copy data must indexed to optimize search speed.
* Media metadata must be stored in Azure Table storage.
* Media files must be stored in Azure BLOB storage.
* The customer-facing website must have access to all ad copy and media.
* The customer-facing website must automatically scale and replicate to locations around the world.
* Media and data must be replicated around the world to decrease the latency of data transfers.
* Media uploads must have fast data transfer rates (low latency) without the need to upload the data offline.
Security
* Customer access must be managed by using Active Directory.
* Media files must be encrypted by using the PlayReady encryption method.
* Customers must be able to upload media quickly and securely over a private connection with no opportunity for internet snooping.