What AI-900 Questions and Answers feature?

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):QnA 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):QnA 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.

Features

The correct completion of the sentence is:

“In a machine learning model, the data that is used as inputs are called features.”

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module “Identify features of common machine learning types,” the term feature refers to an individual measurable property or characteristic of the data that is used by a machine learning model to make predictions or decisions.

In supervised and unsupervised learning, features serve as the inputs to the model. They are the variables that represent the information the algorithm learns from. For example, if a model predicts the price of a house, the features might include square footage, number of bedrooms, location, and age of the property. These features feed into the model so that it can learn the relationship between inputs and outputs.

Microsoft Learn further defines these key concepts:

Features: Input variables (independent variables) used by the model to learn patterns.

Labels: The desired output or target variable that the model is trained to predict (e.g., price, category).

Instances: Individual rows or data records within the dataset (each instance has multiple features).

Functions: Algorithms or mathematical operations used during training and prediction — not data inputs.

Therefore, among the provided options — features, functions, labels, instances — only features accurately describe the data elements used as inputs for training or inference in a machine learning model.

In summary, within the AI-900 learning context:

Features = inputs to the model.

Labels = outputs for supervised learning.

Instances = examples (rows) of data.

Box 1: Entity recognition

the Named Entity Recognition module in Machine Learning Studio (classic), to identify the names of things, such as people, companies, or locations in a column of text.

Named entity recognition is an important area of research in machine learning and natural language processing (NLP), because it can be used to answer many real-world questions, such as:

Which companies were mentioned in a news article?

Does a tweet contain the name of a person? Does the tweet also provide his current location?

Were specified products mentioned in complaints or reviews?

Box 2: Sentiment Analysis

The Text Analytics API ' s Sentiment Analysis feature provides two ways for detecting positive and negative sentiment. If you send a Sentiment Analysis request, the API will return sentiment labels (such as " negative " , " neutral " and " positive " ) and confidence scores at the sentence and document-level.