According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features of classification machine learning", classification is a supervised machine learning technique used when the output variable represents discrete categories or classes. In this case, the brain scan images are already labeled into predefined haemorrhage types, such as "subarachnoid," "epidural," or
"intraventricular." The model's goal is to learn patterns from labeled examples and then predict the correct class for new, unseen images.
The use of categorized brain scan images clearly indicates a supervised learning setup because both the input (image data) and output (haemorrhage type) are known during training. This aligns with Microsoft's definition: classification problems "predict which category or class an item belongs to," often using algorithms such as logistic regression, decision trees, neural networks, or convolutional neural networks (CNNs) for image-based data.
In contrast:
* A. Clustering is an unsupervised learning approach that groups data into clusters based on similarity when no predefined labels exist.
* B. Regression predicts continuous numeric values (e.g., predicting age or temperature), not categories.
Because this project aims to automatically classify medical images into known diagnostic categories, it is a textbook example of classification.
Reference:Microsoft Learn - Identify common types of machine learning models: Regression, Classification, and Clustering (AI-900 Learning Path)

Explanation:
When building a K-means clustering model, all features (variables) used in the model must be numeric in nature. According to the Microsoft Azure AI Fundamentals (AI-900) study materials and standard machine learning theory, K-means clustering is an unsupervised learning algorithm that groups data points into clusters based on their similarity - specifically by minimizing the Euclidean distance between data points and their assigned cluster centroids.
Because the K-means algorithm depends on distance calculations, it requires numeric data types. The Euclidean distance (or similar measures) can only be computed between numerical values. Therefore, all categorical or text data must first be converted into numeric form through feature engineering techniques such as one-hot encoding, label encoding, or embedding vectors, depending on the nature of the data.
Here's how K-means works in summary:
* The algorithm initializes a predefined number of centroids (K).
* Each data point is assigned to the nearest centroid based on numeric distance.
* The centroids are recalculated as the mean of the points in each cluster.
* The process repeats until convergence.
If non-numeric data (e.g., text or Boolean) were provided, the model would not be able to calculate distances accurately, leading to computational errors.
Other options are incorrect:
* Boolean and integer types can represent numeric values but are considered special cases; the algorithm requires general numeric representation (e.g., continuous values).
* Text cannot be processed directly without conversion.
Thus, according to Azure Machine Learning and AI-900 official concepts, all features in a K-means clustering model must be numeric to ensure valid mathematical operations and clustering accuracy.

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) curriculum and Microsoft Learn's modules on Computer Vision, object detection is the AI technique used to identify and locate multiple objects within an image. Unlike simple image classification, which only labels an entire image with a single category (for example, "This is a product"), object detection not only identifies the type of object but also pinpoints its exact position by providing bounding boxes and coordinates within the image.
In the scenario described - identifying the location of products on a conveyor belt - the system must be able to detect multiple items simultaneously and determine their spatial positions. Object detection algorithms (such as YOLO, Faster R-CNN, or SSD) are specifically designed for this purpose. This allows automation systems, like robotic arms or quality inspection systems, to track product locations in real time for sorting, packaging, or defect detection.
Let's evaluate the other options:
* Image classification only determines what is in the image, not where it is located. It cannot handle multiple objects or their positions.
* Image processing involves operations like resizing, filtering, or adjusting contrast, not understanding object placement.
* Optical character recognition (OCR) extracts text from images and documents, unrelated to locating physical items.
Thus, per Microsoft Learn's AI-900 guidance, object detection is the correct computer vision capability when a task requires both identification and spatial localization of items in an image or video stream.
# Final answer Object detection

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", labeling is the process of assigning correct output values (labels) to training data before model training. In supervised learning, every input in the dataset must be paired with its corresponding output so the algorithm can learn the relationship between the two.
In this scenario, the task is to assign classes to images before training a classification model-for example, marking images as "cat," "dog," or "bird." This process defines the target variable (label) that the model will later predict. During training, the classification model uses these labeled examples to learn patterns and distinguish between categories.
Microsoft's official materials clearly define labeling as:
"The process of tagging data with the correct answer so that the model can learn to make predictions." Labeling is a crucial early step in the machine learning lifecycle, especially for image classification and natural language processing (NLP) tasks. Without accurate labels, the model cannot learn correctly and its predictions will be unreliable.
Let's briefly clarify why the other options are incorrect:
* Evaluation refers to testing the model after training to measure accuracy or performance using metrics like precision, recall, or F1 score.
* Feature engineering involves creating or selecting the most relevant input features from raw data but does not involve tagging output labels.
* Hyperparameter tuning adjusts parameters (like learning rate or depth of a tree) to optimize model performance after labeling and training have begun.
Thus, assigning classes to images prior to model training is definitively a Labeling task.

Explanation:

The correct matching aligns directly with the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn modules under "Identify features of Azure Machine Learning". Azure Machine Learning provides a suite of tools that serve different functions within the model development lifecycle - from creating workspaces, to training models, to automating experimentation.
* The Azure portal # Create a Machine Learning workspace.The Azure portal is a web-based graphical interface for managing all Azure resources. According to Microsoft Learn, you use the portal to create and configure the Azure Machine Learning workspace, which acts as the central environment where datasets, experiments, models, and compute resources are organized. Creating a workspace through the portal involves specifying a subscription, resource group, and region - tasks that are part of the setup stage rather than model development.
* Machine Learning designer # Use a drag-and-drop interface used to train and deploy models.The Machine Learning designer (formerly "Azure ML Studio (classic)") provides a visual, no-code/low- code interface for building, training, and deploying machine learning pipelines. The designer uses a drag-and-drop workflow where users connect modules representing data transformations, model training, and evaluation. This tool is ideal for beginners and those who want to quickly experiment with machine learning concepts without writing code.
* Automated machine learning (Automated ML) # Use a wizard to select configurations for a machine learning run.Automated ML simplifies model creation by automatically selecting algorithms, hyperparameters, and data preprocessing options. Users interact through a guided wizard (within the Azure Machine Learning studio) that walks them through configuration steps such as selecting datasets, target columns, and performance metrics. The system then iteratively trains and evaluates multiple models to recommend the best-performing one.
Together, these tools streamline the machine learning workflow:
* Azure portal for setup and resource management,
* Machine Learning designer for visual model creation, and
* Automated ML for guided, automated model selection and tuning.