Correct Answer: A  🗳️

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Correct Answer:

Explanation:

The correct answer is object detection. According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module "Explore computer vision", object detection is the process of identifying and locating objects within an image or video. The primary characteristic of object detection, as emphasized in the study guide, is its ability to return a bounding box around each detected object along with a corresponding label or class.
In this question, the task involves returning a bounding box that indicates the location of a vehicle in an image. This is the exact definition of object detection - identifying that the object exists (a vehicle) and determining its position within the frame. Microsoft Learn clearly differentiates this from other computer vision tasks. Image classification, for example, only determines what an image contains as a whole (for instance, "this image contains a vehicle"), but it does not indicate where in the image the object is located.
Optical character recognition (OCR) is specifically used for extracting printed or handwritten text from images, and semantic segmentation involves classifying every pixel in an image to understand boundaries in greater detail, often used in autonomous driving or medical imaging.
The official AI-900 guide highlights object detection as one of the key computer vision workloads supported by Azure Computer Vision, Custom Vision, and Azure Cognitive Services. These services are designed to detect multiple instances of various object types in a single image, outputting bounding boxes and confidence scores for each.
Therefore, based on the AI-900 official curriculum and Microsoft Learn concepts, returning a bounding box that shows the location of a vehicle is a textbook example of object detection, as it involves both recognition and localization of the object within the image frame.

Correct Answer:

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features of common machine learning types", regression is a supervised machine learning technique used to predict continuous numerical values based on one or more input features. In this scenario, the task is to predict a vehicle's miles per gallon (MPG)-a continuous numeric value-based on several measurable factors such as weight, engine power, and other specifications.
Regression models learn the mathematical relationship between input variables (independent features) and a numeric target variable (dependent outcome). Common regression algorithms include linear regression, decision tree regression, and support vector regression. In the example, the model would analyze historical data of vehicles and learn patterns that map characteristics (like engine size, horsepower, and weight) to fuel efficiency. Once trained, it can predict the MPG for a new vehicle configuration.
The other options describe different problem types:
* Classification predicts discrete categories (for example, whether a car is "fuel efficient" or "not fuel efficient"), not continuous values.
* Clustering is an unsupervised learning method that groups data points based on similarities without predefined labels, not predictive modeling.
* Anomaly detection identifies data points that significantly deviate from normal patterns, such as detecting engine sensor failures or fraudulent transactions.
Since predicting MPG involves estimating a numeric value within a continuous range, regression is the most appropriate model type.
In summary, per AI-900 training content, regression models are used when the output variable is numeric, classification for categorical outputs, and clustering for pattern discovery. Therefore, predicting miles per gallon based on vehicle features is a textbook example of a regression problem in Azure Machine Learning.

Correct Answer: D  🗳️

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