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NEW QUESTION: 1
ユーザーが頻繁に照会するデータベースがあります。
ユーザーは、ピーク営業時間中に、クエリの実行に予想よりも長い時間がかかると報告しています。
データベース管理者が、データベースサーバー上のMicrosoft SQL Server Profilerを使用してセッションアクティビティを追跡します。
トレースを実行している間、データベースサーバーのパフォーマンスが低下し、サーバーがクラッシュします。
クエリの実行時間を収集するための解決策を提案する必要があります。解決策は、データベースサーバーのリソースへの影響を最小限に抑える必要があります。
何をお勧めですか？
A. データベースサーバーのシステムドライブの空き容量を増やしてから、サーバーでSQL Server Profilerを使用してセッションアクティビティを追跡します。
B. パフォーマンスモニターのデータコレクターセット（DCS）を使用してパフォーマンスデータを収集します。
C. tempdbをクリーンアップしてから、データファイルサーバーでSQL Server Profilerを使用してセッションのアクティビティを追跡します。
D. SQL Server Extended Eventsを使用してセッションアクティビティデータを収集します。
Answer: D
Explanation:
Explanation
SQL Server Extended Events has a highly scalable and highly configurable architecture that allows users to collect as much or as little information as is necessary to troubleshoot or identify a performance problem.
Extended Events is a light weight performance monitoring system that uses very few performance resources.
Extended Events provides two graphical user interfaces (New Session Wizard and New Session) to create, modify, display, and analyze your session data.
References:
https://docs.microsoft.com/en-us/sql/relational-databases/extended-events/extended-events?view=sql-server-201

NEW QUESTION: 2
次の要件を満たす必要があるストリーミングソリューションを設計しています。
* AzureIoTハブからの入力データを受け入れます。
*集計データをAzureCosmosDBに書き込みます。
* 5分ごとに最小、最大、および平均のセンサー読み取り値を計算します。
* SQLクエリを使用して計算を定義します。
* Azure Resource Managerテンプレートを使用して、複数の環境にデプロイします。
ソリューションに何を含める必要がありますか？
A. Azure Stream Analytics
B. Sparkストリーミングを使用したAzure HDInsight
C. Azure関数
D. Azure Databricks
Answer: D
Explanation:
Cosmos DB is ideally suited for IoT solutions. Cosmos DB can ingest device telemetry data at high rates.
Architecture

Data flow
Events generated from IoT devices are sent to the analyze and transform layer through Azure IoT Hub as a stream of messages. Azure IoT Hub stores streams of data in partitions for a configurable amount of time.
Azure Databricks, running Apache Spark Streaming, picks up the messages in real time from IoT Hub, processes the data based on the business logic and sends the data to Serving layer for storage. Spark Streaming can provide real time analytics such as calculating moving averages, min and max values over time periods.
Device messages are stored in Cosmos DB as JSON documents.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/iot-using-cosmos-db

NEW QUESTION: 3
ワイヤレスクライアントが2つの異なるワイヤレスコントローラー間でローミングする場合、ネットワーク接続の停止がしばらくの間発生します。この問題を引き起こす構成の問題はどれですか？
A. モビリティグループ内のすべてのコントローラーが同じ仮想インターフェイスIPアドレスを使用しているわけではありません。
B. モビリティグループ内のすべてのコントローラーが同じモビリティグループ名を使用しています。
C. モビリティグループ内のすべてのコントローラーが同じ仮想インターフェイスIPアドレスを使用しています。
D. モビリティグループ内のすべてのコントローラーが同じモビリティグループ名を使用しているわけではありません。
Answer: A
Explanation:
A prerequisite for configuring Mobility Groups is "All controllers must be configured with the same virtual interface IP address". If all the controllers within a mobility group are not using the same virtual interface, inter-controller roaming may appear to work, but the handoff does not complete, and the client loses connectivity for a period of time. -> Answer B is correct.
Reference:
b_cg85/mobility_groups.html

NEW QUESTION: 4
The partial diagram below is an example of what type of database schema design?

A. snowflake schema
B. star schema
C. normalized schema
D. de-normalized schema
Answer: A