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NEW QUESTION: 1
Which information does the Microsoft Dynamics AX development model contain?
A. an element's metadata only
B. an element's metadata and source code
C. an elements source code only
D. the OLTP database
Answer: C

NEW QUESTION: 2
You need to configure the deployment of the storage analysis application. What should you do?
A. Add a new deployment slot.
B. Configure the deployment from source control.
C. Turn on continuous integration.
D. Create a new Mobile Service.
Answer: B
Explanation:
Explanation/Reference:
Explanation:
Scenario: Data analysis results:
The solution must provide a web service that allows applications to access the results of analyses.

NEW QUESTION: 3
You need to ensure that performance requirements for Backtrack reports are met.
What should you recommend? To answer, drag the appropriate technologies to the correct locations. Each technology may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Box 1: Cosmos DB indexes
The report for Backtrack must execute as quickly as possible.
You can override the default indexing policy on an Azure Cosmos container, this could be useful if you want to tune the indexing precision to improve the query performance or to reduce the consumed storage.
Box 2: Cosmos DB TTL
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
Note: With Time to Live or TTL, Azure Cosmos DB provides the ability to delete items automatically from a container after a certain time period. By default, you can set time to live at the container level and override the value on a per-item basis. After you set the TTL at a container or at an item level, Azure Cosmos DB will automatically remove these items after the time period, since the time they were last modified.

NEW QUESTION: 4
オンライン小売業者がAmazon DynamoDBを使用して、顧客トランザクションに関連するデータを保存しています。テーブル内のアイテムには、トランザクションを説明するいくつかの文字列属性と、ショッピングカートやトランザクションに対応するその他の詳細を含むJSON属性が含まれています。平均アイテムサイズは最大250 KBで、そのほとんどはJSON属性に関連付けられています。平均では、1か月あたり最大3GBのデータが生成されます。
顧客はテーブルにアクセスして取引履歴を表示し、必要に応じて取引の詳細を確認します。テーブルに対するクエリの90％は、トランザクション履歴ビューを作成するときに実行されます。
トランザクションの詳細を取得する10％。テーブルはCustomerIDでパーティション分割され、トランザクションデータでソートされます。
クライアントはテーブルにプロビジョニングされた非常に高い読み取り容量を持ち、非常に均一な使用率を経験しますが、他のNoSQLソリューションと比較したAmazon DynamoDBのコストについて不平を言っています。
品質を低下させずに、クライアントの読み取りクエリに関連するコストを削減する戦略はどれですか？
A. TransactionIDでパーティション化するプライマリテーブルを変更し、顧客でパーティション化され、日付でソートされたGSIを作成し、小さな属性をGSIに投影し、GSIにサマリーデータとJSON詳細のプライマリテーブルを照会します。
B. テーブルを垂直方向にパーティション化し、基本属性をプライマリテーブルに格納し、JSONデータを含むセカンダリテーブルへの外部キー参照を作成します。サマリーデータのプライマリテーブルとJSONの詳細のセカンダリテーブルをクエリします。
C. 最終的に一貫した読み取りを使用するようにすべてのデータベース呼び出しを変更し、トランザクション履歴が1秒古くなる可能性があることをお客様に通知します。
D. 日付でソートされたLSIを作成し、JSON属性をインデックスに投影し、サマリーデータのプライマリテーブルとJSON詳細のLSIをクエリします
Answer: B