https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/split-data The correct answer is B. Randomly split the data into rows for training and rows for evaluation.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Describe fundamental principles of machine learning on Azure", the process of developing a machine learning model involves dividing the available dataset into two or more parts-commonly training data and evaluation (or testing) data. The goal is to ensure that the model can learn patterns from one subset of the data (training set) and then be objectively tested on unseen data (evaluation set) to measure how well it generalizes to new situations.
The training dataset contains both features (the measurable inputs) and labels (the target outputs). The model learns from the patterns and relationships between these features and labels. The evaluation dataset also contains features and labels, but it is kept separate during the training phase. Once the model has been trained, it is tested on this unseen evaluation data to calculate metrics like accuracy, precision, recall, or F1 score.
Microsoft emphasizes that the data split should be random and based on rows, not columns. Each row represents a complete observation (for example, one customer record, one transaction, or one image).
Randomly splitting ensures that both subsets represent the same distribution of data, avoiding bias. Splitting by columns would separate features themselves, which would make the model training invalid.
The AI-900 materials often illustrate this using Azure Machine Learning's data preparation workflow, where data is randomly divided (commonly 70% for training and 30% for testing). This ensures the model learns from diverse examples and is fairly evaluated.
Therefore, the verified and correct approach, as per Microsoft's official guidance, is B. Randomly split the data into rows for training and rows for evaluation.

Explanation:

In the context of Microsoft Azure AI Fundamentals (AI-900) and general machine learning principles, regression refers to a type of supervised learning used to predict continuous numerical values based on historical data. The goal of regression is to model the relationship between input variables (features) and a continuous output variable (target).
In this scenario, the task is to predict how many vehicles will travel across a bridge on a given day. The number of vehicles is a numerical value that can vary continuously depending on factors such as time of day, weather, weekday/weekend, or traffic trends. Because the output is numeric and not categorical, this problem type clearly fits into regression analysis.
Microsoft's official learning content for AI-900, under "Identify features of regression and classification machine learning models," specifies that regression models are used to predict values such as sales forecasts, demand estimation, temperature prediction, or traffic volume-all of which share the same underlying objective: predicting a quantity.
To clarify other options:
* Classification is used when predicting categories or discrete classes, such as determining whether an email is spam or not spam, or if an image contains a cat or a dog.
* Clustering is an unsupervised learning technique used to group similar data points without predefined labels (for example, grouping customers by purchasing behavior).
Since predicting the number of vehicles results in a continuous numerical output, it aligns precisely with the regression workload type described in the Microsoft AI-900 study materials.

The correct answer is A. Personally Identifiable Information (PII) detection.
In the Azure AI Language service, PII detection is a built-in feature designed to automatically identify and redact sensitive or confidential information from text data. According to the Microsoft Learn module
"Identify capabilities of Azure AI Language" and the AI-900 study guide, this capability can detect personal data such as names, phone numbers, email addresses, credit card numbers, and other identifiers.
When applied, the service scans input text and either masks or removes these PII elements based on configurable parameters, ensuring compliance with data privacy regulations like GDPR or HIPAA.
For example, if a document contains "John Doe's phone number is 555-123-4567," PII detection can return
"******'s phone number is ***********," thereby preventing exposure of sensitive personal details.
Option analysis:
* A. Personally Identifiable Information (PII) detection: # Correct. It identifies and masks sensitive data in text.
* B. Entity linking: Connects recognized entities to known data sources like Wikipedia; not used for redaction.
* C. Custom text classification: Classifies text into predefined categories; not designed for masking personal data.
* D. Custom named entity recognition (NER): Detects domain-specific entities you define but doesn't automatically mask them.
Therefore, to automate masking of names and phone numbers, the appropriate Azure AI Language feature is PII detection.

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/