最新的Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version) - AI-102 Korean免費考試真題

Explanation:
a tabular form of rows and columns
Rationale
Relational data is, by definition, data that adheres to the relational model .
In the relational model, data is organized into tables , which consist of rows (records or tuples) and columns (fields or attributes). This is known as the tabular form .
The other options are incorrect:
Unstructured data is the opposite of relational data.
A hierarchical folder structure is used for file organization, not the logical structure of relational data.
Comma-separated value (CSV) files can contain relational data, but the core definition of relational data is the tabular structure , not the file format itself.

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Explanation:

First blank: speech_recognition_language
Second blank: add_target_language
speech_translation_config = speechsdk.translation.SpeechTranslationConfig( subscription=os.environ.get( ' SPEECH_KEY ' ), region=os.environ.get( ' SPEECH_REGION ' ) )
# Source (recognition) language: English (US)
speech_translation_config.speech_recognition_language = " en-US "
# Target translation language: Italian
speech_translation_config.add_target_language( " it " )
audio_config = speechsdk.audio.AudioConfig(use_default_microphone= True ) recognizer = speechsdk.translation.TranslationRecognizer( translation_config=speech_translation_config, audio_config=audio_config ) Comprehensive Detailed Explanation along with All References available from Microsoft Azure AI Solution at the end of the explanation To translate speech, configure a SpeechTranslationConfig with (1) the recognition language of the input audio and (2) one or more target languages for translation. In Python, you set the input language with the speech_recognition_language property (e.g., " en-US " ), and you add a target with add_target_language( " < locale > " ) (e.g., " it " for Italian). This exactly matches the SDK guidance and examples for speech translation. Microsoft Learn speech_recognition_language specifies the locale used by the recognizer to understand the spoken input.
add_target_language( " it " ) adds Italian as a translation output language.
Microsoft References
How to translate speech with the Speech SDK (Python examples show translation_config.
speech_recognition_language = from_language and translation_config.add_target_language(...) ). Microsoft Learn SpeechTranslationConfig (Python) reference - add_target_language(language: str) . Microsoft Lea rn Recognize speech (property usage for speech_recognition_language ). Microsoft Learn Language support for Speech service (locales and codes for recognition/translation)

Explanation:

Comprehensive Detailed Explanation
The screenshot shows a Bot Framework Composer dialog that collects user input:
user.name is an entity.
False.
user.name is a property in bot state (a variable), not an entity.
Entities are part of LUIS/NLU models (e.g., PersonName, DateTime).
The dialog asks for a user name and a user age and assigns appropriate values to the user.name and user.age properties.
True.
The dialog explicitly has:
A Bot Asks (Text) step # assigns user's response to user.name .
A Bot Asks (Number) step # assigns response to user.age .
So the values are stored in those properties.
The chatbot attempts to take the first non-null entity value for userName or personName and assigns the value to user.name.
True.
On the right-hand side, the Value expression is:
=coalesce(@userName, @personName)
coalesce returns the first non-null value.
This means if userName entity is available, it is used; otherwise, personName is used.
That result is assigned to user.name .
Correct Answers:
No
Yes
Yes
Microsoft References
Bot Framework Composer - Manage variables and properties
Bot Framework Composer - Recognizers and entities
Coalesce function in Composer expressions

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Explanation:

Scenario Recap:
You have an Azure OpenAI resource with multiple deployed models.
You are using the Chat playground to build a chatbot.
Requirements:
Generate text in concise, formal business language.
Reduce cost of running the model.
Maintain the size of the chatbot history window (i.e., context length).
Step 1: Ensuring concise, formal business language
In the Chat playground setup panel, you can configure System message instructions.
Example:
You are an AI assistant that responds in concise formal business language.
This controls tone, style, and response behavior.
So one setting is: System message / Instructions.
Step 2: Reducing cost while maintaining chat history window
Costs in Azure OpenAI are tied to:
The model used (larger models = more expensive).
The context window size (token usage).
Since we must maintain the size of the chat history window, we cannot shrink context length.
Therefore, to reduce cost: select a smaller/cheaper deployed model (e.g., GPT-3.5 instead of GPT-4).
So the second setting is: Deployment (model selection).
# Final Answer:
Configure Deployment # choose a smaller/cheaper model.
Configure System message instructions # specify "concise formal business language." Microsoft References:
Azure OpenAI Chat playground
Best practices for reducing token cost

Explanation:

In the Azure AI Translator .NET SDK ( Azure.AI.Translation.Text ), the TextTranslationClient exposes TranslateAsync overloads that accept a target language (e.g., " fr " ) and an input string to translate. The call returns a Response < IReadOnlyList < TranslatedTextItem > > . Each TranslatedTextItem includes the automatically detected source language ( DetectedLanguage.Language ) and a collection of translations in Translations . To print the translated text for the first target language, read translation.Translations[0].Text .
Therefore:
Use TranslateAsync(targetLanguage, input) to perform the translation.
Output translation.Translations[0].Text to display the translated result.
Microsoft References
TextTranslationClient.TranslateAsync method (overloads, usage). Microsoft Learn TextTranslationClient class overview. Microsoft Learn TranslatedTextItem model and Translations property (array of translation results).
Quickstart: Translator text client libraries (end-to-end usage pattern). Microsoft Learn

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