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NEW QUESTION: 1
Good project objectives must be:
A. Established without considering resource bounds
B. General rather than specific
C. Realistic and attainable
D. Overly complex
E. Measurable, intangible and verifiable
Answer: C

NEW QUESTION: 2
シナリオ：Citrix Architectは、XenAppおよびXenDesktop環境にアクセスする必要があります。別紙のXenAppおよびXenDesktopの図には、現在のサイトおよびゾーンの設計に関する詳細が示されています。
図を表示するには、[展示]ボタンをクリックします。

サテライトゾーン2のすべてのDelivery Controllerが失敗した場合、どのステートメントが正しいですか？
A. ゾーン2のVirtual Delivery Agent（VDA）マシンはプライマリゾーン内に登録されます。
B. Virtual Delivery Agent（VDA）マシンはローカルホストキャッシュを使用して登録します
C. ゾーン2のVirtual Delivery Agent（VDA）マシンは、サテライトゾーン1内に登録します。
D. サテライトゾーン2の仮想配信エージェント（VDA）マシンは登録されません
Answer: A
Explanation:
Explanation
If a Controller in a satellite zone fails, it fails over to another local Controller, if possible. If no local Controllers are available, it fails over to a Controller in the primary zone.
https://docs.citrix.com/en-us/xenapp-and-xendesktop/7-15-ltsr/manage-deployment/zones.html

NEW QUESTION: 3
会社のヘルプデスクへの内部ユーザーチャットを処理するインテリジェントなボットを作成する予定です。ボットには次の要件があります。
*ユーザーの意味を解釈できる必要があります。
*ユーザーに対して複数のタスクを実行できる必要があります。
*既存のナレッジベースからの質問に答えられる必要がある
要件を満たすソリューションを推奨する必要があります。
各要件に対してどのソリューションを推奨する必要がありますか？回答するには、回答領域で適切なオプションを選択します。
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。

Answer:
Explanation:

Explanation

Box 1: The Language Understanding (LUIS) service
Language Understanding (LUIS) is a cloud-based API service that applies custom machine-learning intelligence to a user's conversational, natural language text to predict overall meaning, and pull out relevant, detailed information.
Box 2: Text Analytics API
The Text Analytics API is a cloud-based service that provides advanced natural language processing over raw text, and includes four main functions: sentiment analysis, key phrase extraction, named entity recognition, and language detection.
Box 3: The QnA Maker service
QnA Maker is a cloud-based Natural Language Processing (NLP) service that easily creates a natural conversational layer over your data. It can be used to find the most appropriate answer for any given natural language input, from your custom knowledge base (KB) of information.
Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-tutorial-dispatch
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/overview/overview