Explanation:

"Optical Character Recognition (OCR) extracts text from handwritten documents." According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features of computer vision workloads," Optical Character Recognition (OCR) is a computer vision capability that enables AI systems to detect and extract printed or handwritten text from images, scanned documents, and photographs.
Microsoft Learn explains that OCR uses machine learning algorithms to analyze visual data, locate regions containing text, and then convert that text into machine-readable digital format. This capability is essential for automating processes such as document digitization, form processing, and data extraction.
OCR technology is provided through services such as the Azure Cognitive Services Computer Vision API and Azure Form Recognizer. The Computer Vision API's OCR feature can extract text from both typed and handwritten sources, including receipts, invoices, letters, and forms. Once extracted, this text can be processed, searched, or stored electronically, enabling automation and efficiency in document management systems.
Let's review the incorrect options:
* Object detection identifies and locates objects in an image by drawing bounding boxes (e.g., detecting vehicles or people).
* Facial recognition identifies or verifies individuals by comparing facial features.
* Image classification assigns an image to one or more predefined categories (e.g., "dog," "car," "tree").
None of these perform the task of extracting textual content from images - that is uniquely handled by Optical Character Recognition (OCR).
Therefore, based on the AI-900 official study content, the verified and correct answer is Optical Character Recognition (OCR), as it specifically extracts text (printed or handwritten) from image-based documents.

In unsupervised machine learning, the algorithm learns patterns or structure within data without pre-labeled outputs or target values. The primary goal is to discover hidden relationships or group similar data points automatically. The Microsoft Azure AI Fundamentals (AI-900) study materials identify clustering as the key example of unsupervised learning.
In clustering, algorithms such as K-means, hierarchical clustering, or DBSCAN group data based on feature similarity. For example, a business may cluster customers by purchase behavior to discover natural customer segments without prior category labels. The model finds inherent patterns within the data rather than being told what to predict.
By contrast, classification and regression are supervised learning techniques. In supervised learning, the algorithm is trained using labeled data where correct outputs are already known. Therefore, the correct answer is B. Clustering, as it best represents unsupervised learning in Azure AI-900 principles.

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) study materials and Microsoft's Responsible AI guidelines, customers must obtain approval based on their intended usage before accessing and deploying Azure OpenAI Service. This requirement ensures that Microsoft upholds its commitment to Responsible AI principles, which include fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.
The Azure OpenAI Service provides access to powerful language models such as GPT series and Codex, which can generate, summarize, and understand natural language and code. Because of the potential for misuse-such as generating harmful content, misinformation, or unethical automation-Microsoft enforces a use case review and approval process before granting customers access to the service. This process involves submitting an application describing the intended purpose, deployment method, and compliance measures.
Only after Microsoft validates that the proposed use aligns with responsible AI practices will access be approved.
This aligns with Microsoft's documented commitment that "customers are required to submit an application that describes their intended use of the Azure OpenAI Service," ensuring that all deployments follow ethical and legal standards. This approval step helps maintain transparency and prevent harmful or non-compliant use cases such as deepfake generation, biased automation, or malicious chatbot deployment.
Other options listed in the question are incorrect:
* Commit to a minimum level of expenditure - Microsoft does not require financial commitments for ethical approval.
* Pay an upfront fee - Payment is handled through normal Azure billing, not a special fee.
* Provide credit card details - Not a responsible AI requirement; this is standard for any Azure subscription.
Therefore, the correct and verified answer per Microsoft's Responsible AI framework and Azure AI-900 study

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.

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials, object detection is a type of computer vision workload that not only identifies objects within an image but also determines their location by drawing bounding boxes around them. This functionality is clearly described in the Microsoft Learn module "Identify features of computer vision workloads." In this scenario, the AI system analyzes an image to find a vehicle and then returns a bounding box showing where that vehicle is located within the image frame. That ability - to detect, classify, and localize multiple objects - perfectly defines object detection.
Microsoft's study content contrasts object detection with other computer vision workloads as follows:
* Image classification: Determines what object or scene is present in an image as a whole but does not locate it (e.g., "this is a car").
* Object detection: Identifies what objects are present and where they are, usually returning coordinates for bounding boxes (e.g., "car detected at position X, Y").
* Optical Character Recognition (OCR): Extracts text content from images or scanned documents.
* Facial detection: Specifically locates human faces within an image or video feed, often as part of face recognition systems.
In Azure, object detection capabilities are available through services such as Azure Computer Vision, Custom Vision, and Azure Cognitive Services for Vision, which can be trained to detect vehicles, products, or other objects in various image datasets.
Therefore, based on the AI-900 study guide and Microsoft Learn materials, the verified and correct answer is Object detection, as it accurately describes the process of returning a bounding box indicating an object's position in an image.