Exam AI-102 topic 1 question 3 discussion

according to https://learn.microsoft.com/en-us/azure/ai-services/language-service/overview Azure AI Language now unifies the Text Analyrics, QnA Maker and LUIS. So in my opinion, the question is obsolete.

Option A (QnA Maker, Language Understanding, and Dispatch): This combination supports chit-chat, knowledge base, multilingual models, and automatic language model selection through Dispatch. Option C (Language Understanding, Text Analytics, and QnA Maker): This option includes Text Analytics for sentiment analysis but lacks Dispatch for automatic model selection. Option D (Text Analytics, Translator, and Dispatch): This option includes Text Analytics and Dispatch but lacks QnA Maker for knowledge bases. Given these considerations, Option A would be the most suitable choice as it covers all requirements through the combined capabilities of QnA Maker, Language Understanding, and Dispatch.

Language Understanding include Sentiment analysis. And use the Bot framework Dispatch CLI tool to help build a process to work with both services(Language Understanding, QnA Maker). So Answer is A https://learn.microsoft.com/en-us/azure/ai-services/luis/luis-reference-prebuilt-sentiment?tabs=english

C is the correct answer

I think the answer option is A (see https://learn.microsoft.com/en-us/azure/ai-services/qnamaker/choose-natural-language-processing-service)

C is right answer.

C is right.

As Luis is outdated (with its Dispatch option), I must be C

The correct answer is A. QnA Maker, Language Understanding, Dispatch. The reason is: QnA Maker: Knowledge Base Support. Answers frequently asked questions and allows interaction. Language understanding (LUIS): Understanding user intents and entities is essential for meaningful answers and message routing. Dispatch: To automatically choose the best language model from user input. It manages numerous language models and selects one for a query. Option B must be revised since translator and speech are unrelated to providing chit-chat, knowledge bases, multilingual models, sentiment analysis, and automatic language model selection. The criteria already cover sentiment analysis; hence, Option C is inappropriate. Text analytics is mainly used to derive insights from text data. QnA Maker supports knowledge bases better than text analytics. Option D must be corrected because text analytics is unrelated to the criteria, and the translator prioritises translation over language model selection.

Language Understanding: An AI service that allows users to interact with your applications, bots, and IoT devices by using natural language. QnA Maker is a cloud-based Natural Language Processing (NLP) service that allows you to create a natural conversational layer over your data. It is used to find the most appropriate answer for any input from your custom knowledge base (KB) of information. Text Analytics: Mine insights in unstructured text using natural language processing (NLP)"no machine learning expertise required. Gain a deeper understanding of customer opinions with sentiment analysis. The Language Detection feature of the Azure Text Analytics REST API evaluates text input. Dispatch uses sample utterances for each of your botג - so this is not required

You should integrate Language Understanding, Text Analytics, and QnA Maker into the chatbot.