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[General] New AI-900 Exam Simulator - AI-900 Guide Torrent

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【General】 New AI-900 Exam Simulator - AI-900 Guide Torrent

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Microsoft Azure AI Fundamentals Sample Questions (Q302-Q307):NEW QUESTION # 302
You need to provide content for a business chatbot that will help answer simple user queries.
What are three ways to create question and answer text by using QnA Maker? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
  • A. Generate the questions and answers from an existing webpage.
  • B. Connect the bot to the Cortana channel and ask questions by using Cortana.
  • C. Import chit-chat content from a predefined data source.
  • D. Use automated machine learning to train a model based on a file that contains the questions.
  • E. Manually enter the questions and answers.
Answer: A,C,E
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore conversational AI in Microsoft Azure," the QnA Maker (now integrated into the Azure AI Language Service as Custom Question Answering) is used to create, train, and publish a knowledge base of question-and-answer pairs that can power a chatbot.
There are three primary methods to create Q&A content:
* Generate questions and answers from an existing webpage (Option A)nA Maker can automatically extract question-answer pairs from structured or semi-structured data sources like FAQs, product manuals, or support webpages.
* Manually enter questions and answers (Option C):Users can create Q&A pairs directly in the QnA Maker portal or Azure Language Studio, enabling custom answers to be crafted manually.
* Import chit-chat content from a predefined data source (Option E)nA Maker provides predefined
"chit-chat" datasets that let a bot handle casual conversation (e.g., greetings or small talk) naturally.
The other options are incorrect:
* B. Use automated machine learning - AutoML is for predictive modeling, not knowledge extraction.
* D. Connect the bot to Cortana - This is a channel integration, not a method of content creation.

NEW QUESTION # 303
You need to predict the income range of a given customer by using the following dataset.

Which two fields should you use as features? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
  • A. First Name
  • B. Age
  • C. Education Level
  • D. Income Range
  • E. Last Name
Answer: B,C
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Describe core concepts of machine learning on Azure", when building a predictive machine learning model, features are the input variables used by the algorithm to predict the target label. The target label is the output or value the model is trained to predict.
In this dataset, the target variable is clearly the Income Range, since the goal is to predict a customer's income bracket. Therefore, Income Range (D) is the label, not a feature. Features must be other attributes that help the model make this prediction.
The fields Education Level (A) and Age (C) are the most relevant features because both can logically and statistically influence income level.
* Education Level is a categorical variable that often correlates strongly with income. Individuals with higher education levels tend to earn more on average, making this an important predictor.
* Age is a numerical variable that typically affects income level due to factors such as experience and career progression.
By contrast:
* First Name (E) and Last Name (B) are irrelevant as features because they are identifiers, not meaningful predictors of income. Including them could lead to bias or model overfitting without contributing to accurate predictions.
Hence, according to AI-900 principles, the features used to train a model predicting income range would be Education Level and Age.

NEW QUESTION # 304
You are developing a solution that uses the Text Analytics service.
You need to identify the main talking points in a collection of documents.
Which type of natural language processing should you use?
  • A. sentiment analysis
  • B. key phrase extraction
  • C. language detection
  • D. entity recognition
Answer: B
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) learning path and Azure Text Analytics service documentation, key phrase extraction is a natural language processing (NLP) technique used to automatically identify the main topics or talking points within a text document or a collection of documents. This feature is designed to summarize textual data by detecting the most relevant words or short phrases that capture the essence of the content.
For example, if a document discusses "renewable energy sources such as solar and wind power," the key phrases extracted might include "renewable energy," "solar power," and "wind power." This helps users quickly understand the primary focus areas of large volumes of text without manual review.
In Azure, the Text Analytics service provides several core NLP capabilities, including:
* Key phrase extraction - identifies main concepts or topics.
* Entity recognition - detects and categorizes proper names like people, locations, or organizations.
* Sentiment analysis - determines the emotional tone (positive, neutral, or negative).
* Language detection - identifies the language used in the text.
Since the question specifies identifying main talking points, the correct feature is key phrase extraction, as it focuses on summarizing themes rather than identifying entities or emotions.
Therefore, the verified answer is B. key phrase extraction.

NEW QUESTION # 305
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:

The Translator service, part of Microsoft Azure Cognitive Services, is designed specifically for text translation between multiple languages. It is a cloud-based neural machine translation service that supports more than 100 languages. According to Microsoft Learn's module "Translate text with the Translator service", this service provides two main capabilities: text translation and automatic language detection.
* "You can use the Translator service to translate text between languages." # YesThis statement is true.
The primary purpose of the Translator service is to translate text accurately and efficiently between supported languages, such as English to Spanish or French to Japanese. It maintains contextual meaning using neural machine translation models.
* "You can use the Translator service to detect the language of a given text." # YesThis statement is also true. The Translator service includes automatic language detection, which determines the source language before translation. For instance, if a user submits text in an unknown language, the service can identify it automatically before performing translation.
* "You can use the Translator service to transcribe audible speech into text." # NoThis statement is false.
Transcribing speech (audio) into text is a function of the Azure Speech service, specifically the Speech- to-Text API, not the Translator service.
Therefore, the Translator service is used for text translation and language detection, while speech transcription belongs to the Speech service.

NEW QUESTION # 306
Select the answer that correctly completes the sentence.

Answer:
Explanation:


NEW QUESTION # 307
......
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