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NEW QUESTION: 1
AWS Data Pipelineのタスクランナーによって呼び出されたときのPollForTaskアクションの役割は何ですか？
A. パイプライン定義を取得するために使用されます。
B. AWS Data Pipelineから実行するタスクを受け取るために使用されます。
C. タスクランナーの進捗状況をAWS Data Pipelineに報告するために使用されます。
D. タスクランナーがタスクを完了したときに結果をAWS Data Pipelineに通知するために使用されます。
Answer: B
Explanation:
Explanation
Task runners call PollForTask to receive a task to perform from AWS Data Pipeline. If tasks are ready in the work queue, PollForTask returns a response immediately. If no tasks are available in the queue, PollForTask uses long-polling and holds on to a poll connection for up to 90 seconds, during which time any newly scheduled tasks are handed to the task agent. Your remote worker should not call PollForTask again on the same worker group until it receives a response, and this may take up to 90 seconds.
http://docs.aws.amazon.com/datapipeline/latest/APIReference/API_PollForTask.html
NEW QUESTION: 2
A. Option C
B. Option B
C. Option D
D. Option A
Answer: A,B,D
NEW QUESTION: 3
많은 웹 서비스를 제공하는 SaaS 회사의 개발자입니다.
회사의 모든 웹 서비스는 다음 요구 사항을 충족해야합니다.
* API 관리를 사용하여 서비스에 액세스
* 인증을 위해 OpenID Connect를 사용하십시오.
* 익명 사용 방지
최근 보안 감사에 따르면 인증없이 여러 웹 서비스를 호출 할 수 있습니다.
어떤 API 관리 정책을 구현해야합니까?
A. 인증 인증서
B. validate-jwt
C. 체크 헤더
D. jsonp
Answer: B
Explanation:
설명:
validate-jwt 정책을 추가하여 모든 수신 요청에 대해 OAuth 토큰을 검증하십시오.
잘못된 답변 :
B : jsonp 정책은 JavaScript 브라우저 기반 클라이언트에서 도메인 간 호출을 허용하기 위해 JSONP (패딩 포함) 지원 JSON을 작업 또는 API에 추가합니다. JSONP는 다른 도메인의 서버에서 데이터를 요청하기 위해 JavaScript 프로그램에서 사용되는 방법입니다. JSONP는 웹 페이지에 대한 액세스가 동일한 도메인에 있어야하는 대부분의 웹 브라우저에서 시행하는 제한을 무시합니다.
JSONP-JavaScript 브라우저 기반 클라이언트에서 도메인 간 호출을 허용하도록 패딩 (JSONP) 지원 JSON을 오퍼레이션 또는 API에 추가합니다.
참고 자료 : https : //docs.microsoft.com/en-us/azure/api-management/api-management-howto-protect-backend-with-aad
NEW QUESTION: 4
You need to recommend the appropriate technology to provide the predictive analytics for passenger pickup.
What should you do?
A. Use HDInsight to analyze the traffic data and write a .NET program to categorize the results.
B. Use Machine Learning Studio to create a predictive model and publish the results as a web service.
C. Use Hadoop on-premises to analyze the traffic and produce a report that shows high traffic zones.
D. Use Power BI to analyze the traffic data and PowerPivot to categorize the results.
Answer: B
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.
