Microsoft Updated AI-900 Exam Questions and Answers by saint

According to the Microsoft Azure AI Fundamentals (AI-900) and Azure Machine Learning documentation, regression is the appropriate machine learning task when the goal is to predict continuous numeric values—such as prices, sales amounts, or other measurable quantities.

In this scenario, the dataset contains motor vehicle sales data, and the objective is to predict vehicle sale values. A vehicle’s sale value (price) is a continuous numeric variable, meaning it can take on a wide range of possible numbers (for example, $15,000, $28,500, $42,300). Regression models are designed to analyze relationships between input features (like make, model, mileage, age, fuel type) and a continuous output variable (price).

Automated Machine Learning (AutoML) in Azure simplifies this process by automatically testing multiple regression algorithms (e.g., Linear Regression, Random Forest, Gradient Boosted Trees) and hyperparameters to find the best-performing model.

Let’s evaluate why the other options are incorrect:

Classification – Used for predicting discrete categories (e.g., car type: sedan, SUV, or truck). Sale value prediction is not categorical.

Time series forecasting – Used when predicting future values based on time-dependent data (e.g., sales over months or years). The question focuses on predicting price based on vehicle characteristics, not over time.

Natural Language Processing (NLP) – Deals with text-based data, not numeric vehicle data.

Computer Vision – Applies to image-based tasks (e.g., detecting car types from photos).

Therefore, per Microsoft Learn’s Automated ML task selection guidance, when predicting a numeric output like vehicle sale value, the correct machine learning task type is Regression.

✅ Final Answer: Regression

The correct answers are B. assigning the color pixels in an image to object names and C. describing the contents of an image.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Describe features of computer vision workloads on Azure,” computer vision is a branch of AI that enables systems to analyze, interpret, and understand visual data from images and videos. It allows machines to identify objects, people, text, and even describe scenes automatically.

Option B: Assigning color pixels in an image to object names represents image classification or object detection, which are key computer vision workloads. In these tasks, AI analyzes pixel patterns to determine which pixels correspond to specific objects (for example, classifying pixels as “car,” “tree,” or “road”).

Option C: Describing the contents of an image corresponds to image captioning, another computer vision workload. It involves using AI models trained to generate natural language descriptions of what is visible in an image, such as “A group of people sitting at a dining table.” Azure’s Computer Vision service provides this functionality through its “Describe Image” API.

Incorrect options:

A. Creating photorealistic images involves generative AI and 3D modeling, not traditional computer vision.

D. Detecting inconsistencies and anomalies in a data stream relates to anomaly detection, not computer vision.

E. Creating visual representations of numerical data involves data visualization, not AI-driven image analysis.

The correct answers are Yes, Yes, and Yes.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn content in the section “Describe features of conversational AI workloads on Azure”, bots created using Azure Bot Service can interact with users across multiple channels. The AI-900 syllabus explains that Azure Bot Service integrates with various communication platforms, allowing developers to build a single bot that can be deployed in many contexts without rewriting the logic.

“You can communicate with a bot by using Cortana.” – Yes.The AI-900 learning materials explain that Cortana, Microsoft’s intelligent personal assistant, can serve as a channel for bots built with the Azure Bot Service. Through the Bot Framework, bots can be connected to Cortana to allow users to interact via voice or text. Although Cortana is less prominent now, it remains conceptually included in the AI-900 coverage as an example of a voice-based conversational AI channel.

“You can communicate with a bot by using Microsoft Teams.” – Yes.This statement is true and directly referenced in the AI-900 syllabus. Microsoft Teams is a fully supported communication channel for Azure Bot Service. Bots in Teams can handle chat messages, commands, and interactions in team or personal contexts. The Microsoft Learn materials specify Teams as one of the native connectors where enterprise users can interact with organizational bots.

“You can communicate with a bot by using a webchat interface.” – Yes.This is also true. The Web Chat channel is one of the most common ways to deploy bots publicly. Azure Bot Service provides a Web Chat control that can be embedded directly into a webpage or web application. This allows users to interact with the bot using a chat window, just like on customer service websites.

Therefore, all three interfaces—Cortana (voice-based), Microsoft Teams (enterprise chat), and Web Chat (browser-based)—are valid and officially supported communication channels for Azure bots.