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NEW QUESTION: 1
注：この質問は、同じシナリオを提示する一連の質問の一部です。連載の各質問には、記載されている目標を達成できる可能性のある固有の解決策が含まれています。他の人が正しい解決策を持っていないかもしれない間、いくつかの質問セットは複数の正しい解決策を持っているかもしれません。
このセクションで質問に答えた後は、それに戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
Table1という名前のテーブルを含むDB1という名前のデータベースがあります。
TablIDに対するすべての更新を監査する必要があります。
解決方法：ローカルディスク上にすべての更新ステートメントをキャプチャするトレースを作成します。
これは目標を達成していますか？
A. いいえ
B. はい
Answer: A
NEW QUESTION: 2
Overview:
Litware, Inc. is a company that manufactures personal devices to track physical activity and other health- related data.
Litware has a health tracking application that sends health-related data from a user's personal device to Microsoft Azure.
Litware has three development and commercial offices. The offices are located in the United States, Luxembourg, and India.
Litware products are sold worldwide. Litware has commercial representatives in more than 80 countries.
Existing Environment:
In addition to using desktop computers in all of the offices, Litware recently started using Microsoft Azure resources and services for both development and operations.
Litware has an Azure Machine Learning solution.
Litware recently extended its platform to provide third-party companies with the ability to upload data from devices to Azure. The data can be aggregated across multiple devices to provide users with a comprehensive view of their global health activity.
While the upload from each device is small, potentially more than 100 million devices will upload data daily by using an Azure event hub.
Each health activity has a small amount of data, such as activity type, start date/time, and end date/time.
Each activity is limited to a total of 3 KB and includes a customer identification key.
In addition to the Litware health tracking application, the users' activities can be reported to Azure by using an open API.
The developers at Litware perform Machine Learning experiments to recommend an appropriate health activity based on the past three activities of a user.
The Litware developers train a model to recommend the best activity for a user based on the hour of the day.
Requirements:
Litware plans to extend the existing dashboard features so that health activities can be compared between the users based on age, gender, and geographic region.
Minimize the costs associated with transferring data from the event hub to Azure Storage.
Litware identifies the following technical requirements:
Data from the devices must be stored for three years in a format that enables the fast processing of
date fields and filtering.
The third-party companies must be able to use the Litware Machine Learning models to generate
recommendations to their users by using a third-party application.
Any changes to the health tracking application must ensure that the Litware developers can run the
experiments without interrupting or degrading the performance of the production environment.
Activity tracking data must be available to all of the Litware developers for experimentation. The developers must be prevented from accessing the private information of the users.
When the Litware health tracking application asks users how they feel, their responses must be reported to Azure.
You need to recommend a data handling solution to support the planned changes to the dashboard.
What is the best recommendation to achieve the goal? More than one answer choice may achieve the goal. Select the BEST answer.
A. anonymization
B. encryption
C. obfuscation
D. compression
Answer: C
Explanation:
Explanation/Reference:
Explanation:
From scenario: Litware plans to extend the existing dashboard features so that health activities can be compared between the users based on age, gender, and geographic region.
The developers must be prevented from accessing the private information of the users.
Dynamic Data Masking can be used to hide or obfuscate sensitive data, by controlling how the data appears in the output of database queries.
Dynamic Data Masking rules can be defined on particular columns, indicating how the data in those columns will appear when queried. There are no physical changes to the data in the database itself; the data remains intact and is fully available to authorized users or applications. Database operations remain unaffected, and the masked data has the same data type as the original data, so DDM can often be applied without making any changes to database procedures or application code.
Reference: https://blogs.technet.microsoft.com/dataplatforminsider/2016/01/25/use-dynamic-data- masking-to-obfuscate-your-sensitive-data/
NEW QUESTION: 3
会社には、オンプレミスのMicrosoft SQL Serverインスタンスがあります。
データエンジニアリングチームは、SQL ServerインスタンスからAzure Blobストレージにデータをコピーするプロセスの実装を計画しています。プロセスは、データのライフサイクルを調整および管理する必要があります。
SQL Serverインスタンスに接続するようにAzure Data Factoryを構成する必要があります。
順番に実行する必要がある3つのアクションはどれですか？回答するには、適切なアクションをアクションのリストから回答エリアに移動し、正しい順序に並べます。
Answer:
Explanation:
Explanation
Step 1: Deploy an Azure Data Factory
You need to create a data factory and start the Data Factory UI to create a pipeline in the data factory.
Step 2: From the on-premises network, install and configure a self-hosted runtime.
To use copy data from a SQL Server database that isn't publicly accessible, you need to set up a self-hosted integration runtime.
Step 3: Configure a linked service to connect to the SQL Server instance.
References:
https://docs.microsoft.com/en-us/azure/data-factory/connector-sql-server