Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features of computer vision workloads on Azure", computer vision models can perform different types of image analysis depending on the goal of the task. The main types include image classification, object detection, and semantic segmentation. Each method analyzes images at a different level of granularity.
* Image Classification # Separate images of polar bears and brown bearsImage classification assigns an entire image to a specific category or label. The model analyzes the image as a whole and determines which predefined class it belongs to. For example, in this case, the model would look at the features of each image and decide whether it shows a polar bear or a brown bear. The Microsoft Learn materials define classification as "assigning an image to a specific category."
* Object Detection # Determine the location of a bear in a photoObject detection identifies where objects appear within an image by drawing bounding boxes around them. This type of model not only classifies what object is present but also provides its location. Microsoft Learn explains that object detection
"detects and locates individual objects within an image." For instance, the model can detect a bear in a forest scene and highlight its position.
* Semantic Segmentation # Determine which pixels in an image are part of a bearSemantic segmentation is the most detailed form of image analysis. It classifies each pixel in an image according to the object it belongs to. In this scenario, the model identifies every pixel corresponding to the bear's body. The AI-
900 content defines this as "classifying every pixel in an image into a category." To summarize:
* Image classification # Categorizes entire images.
* Object detection # Locates and labels objects within images.
* Semantic segmentation # Labels each pixel for precise object boundaries.
https://nanonets.com/blog/how-to-do-semantic-segmentation-using-deep-learning/

Explanation:

The correct order of processes before deploying a model as a service is:
(1) Data preparation # (2) Model training # (3) Model evaluation.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Explore the machine learning process", machine learning follows a structured lifecycle that involves several sequential stages. Before a model can be deployed, the data must be properly prepared, the model must be trained, and then its performance must be evaluated to ensure accuracy and reliability.
* Data Preparation:The first stage involves collecting, cleaning, and transforming raw data into a usable format. Azure Machine Learning provides tools like Data Wrangler, Data Labeling, and Data Transformation pipelines to ensure the dataset is accurate and consistent. As per Microsoft Learn, "data preparation is essential to remove noise, handle missing values, and split the dataset into training and testing sets." This step ensures the model learns from quality input.
* Model Training:In this step, algorithms are applied to the prepared training data to create a predictive model. The system learns patterns and relationships from the data. Azure Machine Learning allows model training using AutoML, custom code, or designer pipelines. The training process produces a model that can make predictions, but it still needs to be tested before deployment.
* Model Evaluation:Once trained, the model's performance is tested against unseen (test) data.
Evaluation metrics like accuracy, precision, recall, and F1-score are analyzed to verify if the model meets business and performance requirements. Microsoft Learn defines this stage as "assessing the model's performance to determine its readiness for deployment." After these three processes, the model can then be deployed as a web service using Azure Machine Learning endpoints. Model retraining happens later when new data becomes available, and data encryption is a security process, not part of model development steps.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Describe features of computer vision workloads on Azure", Optical Character Recognition (OCR) is a core computer vision workload that enables AI systems to detect and extract text from images or scanned documents.
In this scenario, the goal is to identify street names from street signs in photographs. Since the text is embedded within images, OCR is the correct technology to use. OCR works by analyzing the visual patterns of letters, numbers, and symbols, then converting them into machine-readable text. Azure's Computer Vision API and Azure AI Vision Service provide OCR capabilities that can extract printed or handwritten text from pictures, documents, and even real-time camera feeds.
Let's analyze the other options:
* A. Object detection: Identifies and locates objects (like cars, people, or street signs) but not the text written on them.
* C. Image classification: Classifies an entire image into categories (e.g., "street scene" or "traffic sign") but doesn't extract text content.
* D. Facial recognition: Identifies or verifies people by analyzing facial features, unrelated to text extraction.
Therefore, identifying street names on street signs is a text extraction problem, making Optical Character Recognition (OCR) the most accurate and verified answer per Microsoft Learn content.

Explanation:

During model training, a portion of the dataset (commonly 70-80%) is used to teach the machine learning algorithm to identify patterns and relationships between input features and the output label. The remaining data (usually 20-30%) is held back to evaluate the model's performance and verify its accuracy on unseen data. This ensures the model is not overfitted (too tightly fitted to training data) and can generalize well to new inputs.
Key steps highlighted in Microsoft Learn materials:
* Model Training: Use the training data to fit the model - the algorithm learns relationships between input features and labels.
* Model Evaluation: Use the test or validation data to assess the accuracy, precision, recall, or other metrics of the trained model.
* Model Deployment: Once validated, the model is deployed to make real-world predictions.
Other options explained:
* Feature engineering: Involves preparing and transforming input data, not splitting datasets for training and testing.
* Time constraints: Not a machine learning process step.
* Feature stripping: Not a recognized ML concept.
* MLflow models: Refers to an open-source tool for tracking and managing models, not dataset splitting or training.
Thus, when you use a portion of the dataset to prepare and train a machine learning model, and retain the rest to verify results, the process is known as model training.

As described in the AI-900 study guide under "Identify features of machine learning," regression is a supervised learning technique used to predict continuous numerical values. In this scenario, the goal is to predict energy output (a continuous variable) based on density (a numeric input).
Regression models find relationships between variables by fitting a line or curve that best represents the trend of the data. In Azure Machine Learning, regression algorithms such as linear regression, decision tree regression, and boosted decision trees are commonly used for such predictions.
* Classification (A) predicts discrete labels (e.g., "High" or "Low"), not numeric values.
* Clustering (B) groups similar data points but does not perform prediction.
* Knowledge mining (C) extracts insights from unstructured data using tools like Azure AI Search and Cognitive Skills.
Hence, based on AI-900 fundamentals, predicting energy based on density requires a regression workload.