AI-102 무료 덤프문제 온라인 액세스

In Azure Al Studio, you use Completions playground with the GPT-35 Turbo model.
You have a prompt that contains the following code.

You need the model to create an explanation of the code. The solution must minimize costs. What should you do?

• A.Add function F(explanation) to the prompt.
• B.Add// what does function F do? to the prompt.
• C.Change the model to GPT-4-32lc
• D.Set the temperature parameter to 1.

You have an Azure subscription that contains a Microsoft Foundry hub named Hub1, an Azure OpenAI resource named resource1, and a user named User1.
You need to ensure that User1 can create a new Azure Content Understanding in Foundry Tools project in Hub1. The solution must follow the principle of least privilege.
Which role should you assign to User1?

• A.Cognitive Services OpenAI User
• B.Azure AI Developer
• C.Azure AI Administrator
• D.Cognitive Services OpenAI Contributor

You have an Azure subscription.
You plan to build a solution That will analyze scanned documents and export relevant fields to a database.
You need to recommend which Azure Al service to deploy for the following types of documents:
* Internal expenditure request authorization forms
* Supplier invoices
The solution must minimize development effort.
What should you recommend for each document type? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point

정답:

Explanation:

Azure AI Document Intelligence offers pre-built models for common document types like invoices, which extract key fields (e.g., vendor, amount, date) with minimal setup. For custom forms like internal authorization requests, the pre-built general document model handles layout and text extraction, but to minimize development effort, use the pre-built invoice model where applicable and a custom model only if pre-built options fall short. However, the standard recommendation prioritizes pre-built for both to avoid training.

You Have a chatbot that uses the Azure Al Language custom question answering service. The model used by the service was trained by using an internal support FAQ document.
You discover that the chatbot fails to provide correct answers to common questions.
You need to increase the accuracy of the responses provided by the chatbot. The solution must minimize development effort.
Which three actions should you perform in sequence from Language Studio? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

정답:

Explanation:

Answer Area
Open the Review suggestions pane.
This step allows you to access suggestions generated by the Azure AI Language service based on user interactions and queries. Reviewing these suggestions helps identify potential improvements or alternative phrasings that can enhance the model ' s understanding and accuracy with minimal effort.
Review and accept the alternative phrases.
After opening the review suggestions pane, you can evaluate and accept alternative phrases or questions that users might have asked. Accepting these updates trains the model to recognize variations in how questions are posed, thereby improving its ability to provide accurate responses without requiring extensive manual retraining.
Enable active learning.
Enabling active learning allows the model to continuously improve by learning from user interactions and feedback over time. This feature automatically suggests refinements based on real-world usage, minimizing development effort while enhancing the chatbot ' s accuracy as it adapts to common questions.
Rationale
These steps are sequenced to progressively improve the chatbot ' s performance with minimal development effort. Starting with reviewing suggestions leverages existing data, accepting alternative phrases refines the model ' s understanding, and enabling active learning ensures ongoing improvement, aligning with the goal of increasing accuracy efficiently.

You are building an app that will translate speech by using the Azure Al Language service.
You need configure the app to translate the speech from English to Italian.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

정답:

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)