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NEW QUESTION: 1
Solutions Architect는 VPN 연결을 통해 온 프레미스 데이터 센터에 연결할 AWS에서 애플리케이션을 설계하고 있습니다. 솔루션은 VPN을 통해 네트워크 트래픽을 기록할수 있어야합니다.
이 네트워크 트래픽을 기록하는 서비스는 무엇입니까?
A. Amazon S3 버킷 로그
B. AWS CloudTrail 로그
C. Amazon CloudWatch Logs
D. Amazon VPC 흐름 로그
Answer: D
Explanation:
Explanation
VPC Flow Logs is a feature that enables you to capture information about the IP traffic going to and from network interfaces in your VPC. Flow log data can be published to Amazon CloudWatch Logs and Amazon S3. After you've created a flow log, you can retrieve and view its data in the chosen destination. Refer:
https://docs.aws.amazon.com/vpc/latest/userguide/flow-logs.html

NEW QUESTION: 2
You need to design the solution for analyzing customer data.
What should you recommend?
A. Azure Cognitive Services
B. Azure Databricks
C. Azure Batch
D. Azure SQL Data Warehouse
E. Azure Data Lake Storage
Answer: B
Explanation:
Explanation
Customer data must be analyzed using managed Spark clusters.
You create spark clusters through Azure Databricks.
References:
https://docs.microsoft.com/en-us/azure/azure-databricks/quickstart-create-databricks-workspace-portal
Topic 2, Trey Research Case study
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other question on this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next sections of the exam. After you begin a new section, you cannot return to this section.
To start the case study
To display the first question on this case study, click the button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the button to return to the question.
Background
Trey Research is a technology innovator. The company partners with regional transportation department office to build solutions that improve traffic flow and safety.
The company is developing the following solutions:

Regional transportation departments installed traffic sensor systems on major highways across North America.
Sensors record the following information each time a vehicle passes in front of a sensor:
* Time
* Location in latitude and longitude
* Speed in kilometers per second (kmps)
* License plate number
* Length of vehicle in meters
Sensors provide data by using the following structure:

Traffic sensors will occasionally capture an image of a vehicle for debugging purposes.
You must optimize performance of saving/storing vehicle images.
Traffic sensor data
* Sensors must have permission only to add items to the SensorData collection.
* Traffic data insertion rate must be maximized.
* Once every three months all traffic sensor data must be analyzed to look for data patterns that indicate sensor malfunctions.
* Sensor data must be stored in a Cosmos DB named treydata in a collection named SensorData
* The impact of vehicle images on sensor data throughout must be minimized.
Backtrack
This solution reports on all data related to a specific vehicle license plate. The report must use data from the SensorData collection. Users must be able to filter vehicle data in the following ways:
* vehicles on a specific road
* vehicles driving above the speed limit
Planning Assistance
Data used for Planning Assistance must be stored in a sharded Azure SQL Database.
Data from the Sensor Data collection will automatically be loaded into the Planning Assistance database once a week by using Azure Data Factory. You must be able to manually trigger the data load process.
Privacy and security policy
* Azure Active Directory must be used for all services where it is available.
* For privacy reasons, license plate number information must not be accessible in Planning Assistance.
* Unauthorized usage of the Planning Assistance data must be detected as quickly as possible.
Unauthorized usage is determined by looking for an unusual pattern of usage.
* Data must only be stored for seven years.
Performance and availability
* The report for Backtrack must execute as quickly as possible.
* The SLA for Planning Assistance is 70 percent, and multiday outages are permitted.
* All data must be replicated to multiple geographic regions to prevent data loss.
* You must maximize the performance of the Real Time Response system.
Financial requirements
Azure resource costs must be minimized where possible.

NEW QUESTION: 3
ケーススタディ3-Proseware、Inc
バックグラウンド
あなたはProseware、Incの開発者です。Prosewareの内部サービス、外部サービス、およびアプリケーションに一連のガバナンスポリシーを適用するアプリケーションを開発しています。アプリケーションはまた、一般的な機能のための共有ライブラリを提供します。
必要条件
政策サービス
ポリシーサービスという名前のステートフルASP.NET Core 2.1 Webアプリケーションを開発してAzure App Service Webアプリにデプロイします。アプリケーションは、Azure Event Gridからのイベントに反応し、それらのイベントに基づいてポリシーアクションを実行します。
アプリケーションでは、すべてのApplication InsightsテレメトリにEvent Grid Event IDフィールドを含める必要があります。
ポリシーサービスは、Application Insightsを使用して、実行中のポリシーアクションの数に応じて自動的にスケーリングする必要があります。
ポリシー
ログポリシー
すべてのAzure App Service Webアプリは、Azure Blobストレージにログを書き込む必要があります。すべてのログファイルは、logdropという名前のコンテナーに保存する必要があります。ログは15日間コンテナに保持する必要があります。
認証イベント
認証イベントは、ユーザーのサインインとサインアウトを監視するために使用されます。すべての認証イベントは、ポリシーサービスによって処理される必要があります。サインアウトはできるだけ早く処理する必要があります。
PolicyLib
すべてのASP.NET Core Webサービスとアプリケーションに共通の機能を含むPolicyLibという名前の共有ライブラリがあります。 PolicyLibライブラリは次の条件を満たす必要があります。
* Application Insightsテレメトリからユーザー以外のアクションを除外します。
* Webサービスがそれ自体をスケーリングできるようにするメソッドを提供する
*スケーリングアクションがアプリケーションの使用を妨げないことを確認する
その他の
異常検知サービス
異常のログ情報を分析する異常検出サービスがあります。 Azure Machine Learningモデルとして実装されます。モデルはWebサービスとしてデプロイされます。
異常が検出された場合、管理者にメールを送信するAzure関数がHTTP WebHookを使用して呼び出されます。
健康モニタリング
すべてのWebアプリケーションおよびサービスには、/ healthサービスエンドポイントでヘルスモニタリングがあります。
ポリシーの喪失
ポリシーサービスを展開するときに、展開中に適用されている途中のポリシーは適用されない場合があります。
パフォーマンスの問題
負荷が高い場合、異常検出サービスはスローダウンし、接続を拒否します。
通知の待ち時間
ユーザーは、異常が検出されてから数分後に異常検出メールが届く場合があると報告しています。
アプリファイルの関連部分を以下に示します。行番号は参照用にのみ含まれており、それらが属する特定のファイルを示す2文字の接頭辞が含まれています。


アプリファイルの関連部分を以下に示します。行番号は参照用にのみ含まれており、それらが属する特定のファイルを示す2文字の接頭辞が含まれています。

通知の待ち時間の問題を解決する必要があります。
どの2つのアクションを実行する必要がありますか？それぞれの正解は、ソリューションの一部を示しています。
注：それぞれの正しい選択は1ポイントの価値があります。
A. Always Onをfalseに設定します。
B. 常にオンに設定します。
C. Azure FunctionがApp Serviceプランを使用していることを確認します。
D. Azure Functionが消費プランを使用するように設定されていることを確認します。
Answer: B,C
Explanation:
Azure Functions can run on either a Consumption Plan or a dedicated App Service Plan. If you run in a dedicated mode, you need to turn on the Always On setting for your Function App to run properly. The Function runtime will go idle after a few minutes of inactivity, so only HTTP triggers will actually "wake up" your functions. This is similar to how WebJobs must have Always On enabled.
Scenario: Notification latency: Users report that anomaly detection emails can sometimes arrive several minutes after an anomaly is detected.
Anomaly detection service: You have an anomaly detection service that analyzes log information for anomalies. It is implemented as an Azure Machine Learning model. The model is deployed as a web service.
If an anomaly is detected, an Azure Function that emails administrators is called by using an HTTP WebHook.
References:
https://github.com/Azure/Azure-Functions/wiki/Enable-Always-On-when-running-on-dedicated- App-Service-Plan