AI-900 Exam Dumps - Microsoft Azure AI Fundamentals

Box1: Sentiment analysis

Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.

Box 2: Broad entity extraction

Broad entity extraction: Identify important concepts in text, including key

Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.

Box 3: Entity Recognition

Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.

According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module “Explore fundamental principles of machine learning,” regression is a supervised machine learning technique used to predict continuous numeric values based on input data.

In this scenario, the goal is to predict how many hours of overtime a delivery person will work depending on the number of orders received. The output — the number of overtime hours — is a continuous variable (for example, 1.5 hours, 3.2 hours, etc.), not a category. This makes it a regression problem, where the model learns patterns from historical data and uses those patterns to estimate a continuous numeric outcome.

Why Regression Applies Here:

Regression models work by finding the mathematical relationship between input features (independent variables) and output values (dependent variables). In this case:

Input (feature): Number of orders received

Output (label): Predicted overtime hours

Azure Machine Learning supports several regression algorithms, including Linear Regression, Decision Tree Regression, and Neural Network Regression, all of which can handle scenarios where a numeric prediction is required.

Why Not the Other Options:

Classification: Used for predicting discrete categories or labels (e.g., “on-time” vs. “late”). It does not output continuous numbers.

Clustering: An unsupervised learning technique used to group data points with similar characteristics, not to make numeric predictions.

Thus, when the output variable is a numeric prediction (such as hours, prices, quantities, or time), the correct machine learning task is Regression.

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.

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.

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.

Azure Bot Service and Azure Cognitive Services can be integrated. → Yes

Azure Bot Service engages with customers in a conversational manner. → Yes

Azure Bot Service can import frequently asked questions (FAQ) to question and answer sets. → Yes

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All three statements are true, as confirmed by the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Explore conversational AI.” The Azure Bot Service is Microsoft’s platform for building, deploying, and managing intelligent bots that can communicate naturally with users across various channels (web, Teams, Facebook Messenger, etc.).

Azure Bot Service and Azure Cognitive Services can be integrated → YesMicrosoft Learn specifies that Azure Bot Service can be enhanced with Azure Cognitive Services such as Language Understanding (LUIS), QnA Maker, and Speech Services to add intelligence. For example, integration with LUIS allows bots to understand user intent and context, while QnA Maker helps them respond accurately to FAQs. As stated in the official documentation: “The Azure Bot Service can be combined with Cognitive Services to create bots that understand language, speech, and meaning.”

Azure Bot Service engages with customers in a conversational manner → YesThe primary function of Azure Bot Service is to create conversational AI agents that interact naturally with users. These bots simulate human-like dialogue using text or speech. According to Microsoft Learn, “Bots created using Azure Bot Service communicate with users in a conversational format through natural language.”

Azure Bot Service can import frequently asked questions (FAQ) to question and answer sets → YesAzure Bot Service can integrate with the QnA Maker (now part of Azure Cognitive Service for Language) to automatically import FAQs from existing documents or web pages and generate a knowledge base of question-answer pairs. This allows the bot to respond intelligently to customer queries.

In conclusion, Azure Bot Service supports intelligent, conversational interaction, integrates seamlessly with Cognitive Services, and can use QnA Maker to import and manage FAQ-based knowledge sets—making all three statements true.

Yes, Yes, No.

According to the Microsoft Azure AI Fundamentals (AI-900) study materials, conversational AI enables applications, websites, and digital assistants to interact with users via natural language. A chatbot is a key conversational AI workload and can be integrated into multiple channels such as web pages, Microsoft Teams, Facebook Messenger, and Cortana using Azure Bot Service and Bot Framework.

“A restaurant can use a chatbot to answer queries through Cortana” — Yes.Azure Bot Service supports multi-channel deployment, which includes Cortana integration. This means the same bot can respond to voice or text input via Cortana, making it a valid use case for a restaurant to provide menu details, reservations, or order tracking through voice-based AI assistants.

“A restaurant can use a chatbot to answer inquiries about business hours from a webpage” — Yes.This is a standard scenario for chatbots embedded on a company website. As per Microsoft Learn’s Describe features of conversational AI module, a chatbot can be added to a website to handle FAQs such as business hours, location, or menu details, thereby improving response time and reducing repetitive human workload.

“A restaurant can use a chatbot to automate responses to customer reviews on an external website” — No.Azure bots and other conversational AI tools cannot automatically interact with or post on external third-party platforms where the business does not control the data or API integration. Automated posting or replying to reviews on external review sites (e.g., Yelp or Google Reviews) would violate both ethical and technical boundaries of responsible AI usage outlined by Microsoft.

The correct answers are A and D because both scenarios involve extracting structured information from documents, which is exactly what Azure Form Recognizer is designed to do.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Explore computer vision”, Form Recognizer is an Azure Cognitive Service that uses advanced Optical Character Recognition (OCR) and machine learning to extract key-value pairs, tables, and text from structured and semi-structured documents such as receipts, invoices, business cards, and forms. It allows organizations to automate data entry and digitize document processing.

A. Extract the invoice number from an invoice → CorrectForm Recognizer can identify fields such as invoice number, total amount, date, vendor name, and billing address directly from invoices. It uses prebuilt models for invoices and receipts that automatically detect and extract relevant information without requiring extensive manual labeling. As stated in Microsoft Learn, “Form Recognizer extracts information from documents like receipts and invoices and returns structured data including key-value pairs.”

D. Identify the retailer from a receipt → CorrectThe prebuilt receipt model in Form Recognizer can read printed or scanned receipts and extract data points such as retailer name, transaction date, total amount, and tax information. This makes it ideal for expense reporting, auditing, or financial reconciliation.

The following options are incorrect:

B. Translate a form from French to English → This task involves language translation, which is performed by Azure Translator, not Form Recognizer.

C. Find an image of a product in a catalog → This requires object detection or image classification, which are part of Computer Vision, not Form Recognizer.

Therefore, based on Microsoft’s AI-900 learning objectives and documentation, the two correct scenarios are:

✅ A. Extract the invoice number from an invoice

✅ D. Identify the retailer from a receipt