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NEW QUESTION: 1

A. Option C
B. Option A
C. Option E
D. Option D
E. Option B
Answer: D,E

NEW QUESTION: 2
Which card has the resources to run multiple servise?
A. system management card
B. redundant crossbar card
C. switch processor I/O card
D. packet service card
Answer: D

NEW QUESTION: 3
A performance test team was testing the capacity of concurrent users of a web application deployed on a multi-processor server. While testing 250 concurrent users, the administrator discovers connections to the database are failing.
In response, a system administrator increased the connection pool to the maximum limit which the database allows and increased the prepared statement cache size. The test passed, but the application response time was slower.
How should the system administrator tune the data source properties to improve the application response time?
A. Change connection pools purge policy.
B. Decrease number of free pool partitions.
C. Decrease the prepared statement cache size.
D. Decrease connection pool reap time.
Answer: C
Explanation:
Explanation/Reference:
Explanation:
The higher the statement cache, the more system resources are delayed. Therefore, if you set the number too high, you might lack resources because your system cannot open multiple prepared statements.
References: http://www.ibm.com/support/knowledgecenter/en/SSAW57_8.5.5/com.ibm.websphere.nd.doc/ ae/udat_jdbcdatasorprops.html

NEW QUESTION: 4
5つの言語をサポートするボットを作成する予定です。ボットは、3つの異なる国にいるユーザーによって使用されます。ボットは、一般的な顧客の質問に答えます。ボットは、言語理解（LUIS）を使用して、使用するスキルを識別し、顧客の言語を検出します。
計画されたボット用に作成する必要があるAzureリソースの最小数を特定する必要があります。
QnA Maker、LUIS、および言語検出のインスタンスをいくつ作成する必要がありますか？回答するには、回答エリア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には1ポイントの価値があります。

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