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NEW QUESTION: 1
You plan to create a bot that will support five languages. The bot will be used by users located in three different countries. The bot will answer common customer questions. The bot will use Language Understanding (LUIS) to identify which skill to use and to detect the language of the customer.
You need to identify the minimum number of Azure resources that must be created for the planned bot.
How many QnA Maker, LUIS and Language Detection instances should you create? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
QnA Maker: 5
If the user plans to support multiple languages, they need to have a new QnA Maker resource for each language.
LUIS: 5
If you need a multi-language LUIS client application such as a chatbot, you have a few options. If LUIS supports all the languages, you develop a LUIS app for each language. Each LUIS app has a unique app ID, and endpoint log. If you need to provide language understanding for a language LUIS does not support, you can use Microsoft Translator API to translate the utterance into a supported language, submit the utterance to the LUIS endpoint, and receive the resulting scores.
Language detection: 1
The Language Detection feature of the Azure Text Analytics REST API evaluates text input for each document and returns language identifiers with a score that indicates the strength of the analysis.
This capability is useful for content stores that collect arbitrary text, where language is unknown. You can parse the results of this analysis to determine which language is used in the input document. The response also returns a score that reflects the confidence of the model. The score value is between 0 and 1.
The Language Detection feature can detect a wide range of languages, variants, dialects, and some regional or cultural languages. The exact list of languages for this feature isn't published.
References:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/overview/language-support
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-language-support
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-language-detection

NEW QUESTION: 2
Which statements about the Route Target Rewrite feature are not true? (Choose two.)
A. Route target replacement can be done on incoming and outgoing BGP updates.
B. Route Target Rewrite allows the administration of the routing policy to stay local to the AS.
C. After the route target replacement policy is applied to the target router, the BGP session need not be refreshed manually to put the policy into operation.
D. Route target replacement is always done by the ASBR.
E. Route target rewrites are supported for VRF export maps.
Answer: C,D
Explanation:
The MPLS VPN-Route Target Rewrite feature allows the replacement of route targets on incoming and outgoing Border Gateway Protocol (BGP) updates. Typically, Autonomous System Border Routers (ASBRs) perform the replacement of route targets at autonomous system boundaries. Route Reflectors (RRs) and provider edge (PE) routers can also perform route target replacement.
The main advantage of the MPLS VPN - Route Target Rewrite feature is that it keeps the administration of routing policy local to the autonomous system.

NEW QUESTION: 3
次の自動スケール条件を含むApp1という名前のAzure Webアプリがあります。自動作成されたデフォルトのスケール条件には、特定のインスタンスカウントへのスケールが2に設定されたスケールモードがあります。
スケール条件1の構成は次のとおりです。
*スケールモード：特定のインスタンスカウントにスケーリング
*インスタンス数3
*スケジュール：開始日/終了日を指定します
*開始日：2019年8月1日
*終了日：2019年9月1日、18：00
スケール条件2の構成は次のとおりです。
*スケールモード：特定のインスタンスカウントにスケーリング
*インスタンス数4
*スケジュール：特定の日を繰り返す
*繰り返し：月曜日
*開始時間：06:00
*終了時間：18:00
スケール条件3の構成は次のとおりです。

Answer:
Explanation:

Explanation

Box 1: 5
Scale condition 1, Scale condition 2, and Scale condition 3 applies.
Scale condition 3 takes precedence as it the largest increase in the number of instances.
Box 2: 5
Scale condition 1 does not apply as its end date is exceeded.
Scale condition 2 and Scale condition 3 applies.
Scale condition 3 takes precedence as it the largest increase in the number of instances.
When you configure multiple policies and rules, they could conflict with each other. Autoscale uses the following conflict resolution rules to ensure that there is always a sufficient number of instances running:
* Scale-out operations always take precedence over scale-in operations.
* When scale-out operations conflict, the rule that initiates the largest increase in the number of instances takes precedence.
* When scale in operations conflict, the rule that initiates the smallest decrease in the number of instances takes precedence.
References:
https://docs.microsoft.com/en-us/azure/architecture/best-practices/auto-scaling