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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。
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あなたの会社は、Microsoft Azure SQL Databaseインスタンスのデータにアクセスする新しいビジネスインテリジェンスアプリケーションを開発しています。インスタンス内のすべてのオブジェクトは同じ所有者を持ちます。
BI_Userという名前の新しいセキュリティプリンシパルには、データベースでストアドプロシージャを実行するためのアクセス許可が必要です。ストアドプロシージャは、データベース内のテーブルとの間で読み書きを行います。どのストアード・プロシージャーもIDENTIFY_INSERT操作または動的SQLコマンドを実行しません。
権限の範囲とBI_Userの認証はデータベースに限定されるべきです。許可を与えるときは、最小特権の原則を使用する必要があります。
必要なセキュリティプリンシパルを作成して適切な権限を付与する必要があります。
解決方法：次のTransact-SQLステートメントを実行します。
解決策は目標を満たしていますか？
A. はい
B. いいえ
Answer: A
Explanation:
Explanation
One method of creating multiple lines of defense around your database is to implement all data access using stored procedures or user-defined functions. You revoke or deny all permissions to underlying objects, such as tables, and grant EXECUTE permissions on stored procedures. This effectively creates a security perimeter around your data and database objects.
Best Practices
Simply writing stored procedures isn't enough to adequately secure your application. You should also consider the following potential security holes.
* Grant EXECUTE permissions on the stored procedures for database roles you want to be able to access the data.
* Revoke or deny all permissions to the underlying tables for all roles and users in the database, including the public role. All users inherit permissions from public. Therefore denying permissions to public means that only owners and sysadmin members have access; all other users will be unable to inherit
* permissions from membership in other roles.
* Do not add users or roles to the sysadmin or db_owner roles. System administrators and database owners can access all database objects.
References:
https://docs.microsoft.com/en-us/dotnet/framework/data/adonet/sql/managing-permissions-with-stored-procedure
NEW QUESTION: 2
Huawei RH8100 V3 cascades two 4-socket servers into one 8-socket server.
A. FALSE
B. TRUE
Answer: A
NEW QUESTION: 3
You are designing an Azure Data Factory pipeline for processing data. The pipeline will process data that is stored in general-purpose standard Azure storage.
You need to ensure that the compute environment is created on-demand and removed when the process is completed.
Which type of activity should you recommend?
A. Databricks Jar activity
B. HDInsight Pig activity
C. Data Lake Analytics U-SQL activity
D. Databricks Python activity
Answer: B
Explanation:
The HDInsight Pig activity in a Data Factory pipeline executes Pig queries on your own or on-demand HDInsight cluster.
References:
https://docs.microsoft.com/en-us/azure/data-factory/transform-data-using-hadoop-pig