Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Identify guiding principles for responsible AI," Fairness is one of Microsoft's six core principles of Responsible AI. The principle of fairness ensures that AI systems treat all individuals and groups equitably, and that the models do not produce biased or discriminatory outcomes.
Bias in AI systems can occur when training data reflects existing prejudices, inequalities, or imbalances. For example, if a dataset used for a hiring model underrepresents a certain demographic group, the AI system might produce unfair recommendations. Microsoft emphasizes that AI should not reflect or reinforce bias and that developers must actively design, test, and monitor models to mitigate unfairness.
Microsoft's Six Responsible AI Principles:
* Fairness - AI systems should treat everyone equally and avoid bias.
* Reliability and safety - AI systems must operate as intended even under unexpected conditions.
* Privacy and security - AI must protect personal and business data.
* Inclusiveness - AI should empower all people and be accessible to diverse users.
* Transparency - AI systems should be understandable and their decisions explainable.
* Accountability - Humans should be accountable for AI system outcomes.
The other options do not fit this context:
* Accountability ensures human responsibility for AI decisions.
* Inclusiveness focuses on accessibility and empowering all users.
* Transparency relates to making AI systems understandable.
Therefore, the correct answer is fairness, as it directly addresses the principle that AI systems should NOT reflect biases from the datasets used to train them.

The correct answers are B. Azure REST API and C. Azure SDK for Python.
The Azure OpenAI Service can be accessed using multiple development interfaces. According to Microsoft Learn documentation, developers can call the service via the Azure REST API, which provides direct HTTPS- based access to the model endpoints for tasks like completions, chat, embeddings, and image generation. This interface is platform-independent and supports integration with any system capable of making HTTP requests.
Additionally, Azure SDKs offer higher-level libraries for convenient integration into applications. The Azure SDK for Python and Azure SDK for JavaScript are both supported for Azure OpenAI interaction, allowing developers to authenticate with Azure credentials, send prompts, and receive model responses programmatically.
However, among the listed options, the REST API (B) and SDK for Python (C) are most explicitly referenced in the AI-900 learning modules and Microsoft documentation as standard tools to call Azure OpenAI services.
Option A (Azure CLI) is incorrect because the CLI is used primarily for provisioning and managing Azure resources, not for directly calling OpenAI model endpoints.
Therefore, based on the Azure AI-900 and OpenAI integration guidance, the correct answers are B. Azure REST API and C. Azure SDK for Python.

rrect answer is C. Predicting the sale price of a house based on historical data, the size of the house, and the number of bedrooms.
In machine learning, regression is a supervised learning technique used to predict continuous numeric values.
Microsoft's AI-900 study guide defines regression models as those that estimate relationships between variables-predicting a continuous outcome variable from one or more input features.
In this case, the house sale price is a continuous numeric value, and inputs such as size, location, and number of bedrooms are the features. Common regression algorithms include linear regression, decision tree regression, and boosted regression.
Other options represent different ML workloads:
* A involves segmentation by categories (classification or clustering).
* B represents clustering, grouping similar items without predefined labels.
* D represents computer vision, counting animals in images rather than predicting a numeric value.
Hence, the verified answer is C. Regression.

The label is the column you want to predict. The identified Features are the inputs you give the model to predict the Label.
Example:
The provided data set contains the following columns:
vendor_id: The ID of the taxi vendor is a feature.
rate_code: The rate type of the taxi trip is a feature.
passenger_count: The number of passengers on the trip is a feature.
trip_time_in_secs: The amount of time the trip took. You want to predict the fare of the trip before the trip is completed. At that moment, you don't know how long the trip would take. Thus, the trip time is not a feature and you'll exclude this column from the model.
trip_distance: The distance of the trip is a feature.
payment_type: The payment method (cash or credit card) is a feature.
fare_amount: The total taxi fare paid is the label.
Reference:
https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/predict-prices

According to the Microsoft Azure AI Fundamentals (AI-900) curriculum and Microsoft Learn's "Explore Automated Machine Learning in Azure Machine Learning" module, Automated ML (AutoML) is the Azure Machine Learning capability that allows users to quickly build, train, and deploy predictive models with minimal or no coding experience.
Automated ML automatically performs tasks that would normally require expert data science knowledge, such as:
* Selecting appropriate algorithms (e.g., decision trees, logistic regression, random forests)
* Performing hyperparameter tuning to optimize model accuracy
* Handling missing data and feature scaling automatically
* Generating performance metrics and best model recommendations
This feature is especially useful for business analysts, developers, or beginners who want to leverage machine learning for predictions (like sales forecasting, churn analysis, or demand prediction) without having to write complex Python code.
Other options explained:
* A. ML pipelines automate and organize workflows for model training and deployment but still require pre-built models.
* B. Copilot is an AI-powered assistant embedded in Microsoft tools for productivity, not a model training feature.
* C. DALL-E is an image generation model under Azure OpenAI, not a predictive modeling tool.
Thus, per official Microsoft Learn content, Automated Machine Learning is the correct capability to quickly build and deploy predictive models with minimal coding.