Microsoft Azure AI Fundamentals AI-900 Practice Question

Ability to generate new content based on learned data patterns - This is the correct answer. Generative AI solutions are specifically designed to produce new content by learning and emulating the patterns within the data they are trained on. This can include creating text, images, or other forms of content that resemble the original data, making this feature a primary aspect of generative AI.

Classification of data into predefined categories - Classification is a feature of discriminative models, which focus on identifying which category or label a given input belongs to. Generative AI, by contrast, is about creating new content rather than categorizing it. While classification is important in machine learning, it does not describe generative AI.

Detection of anomalies in real-time data streams - Anomaly detection typically involves identifying data points that do not fit within the expected patterns, often using discriminative models or specific anomaly detection algorithms. Generative AI, however, generates new data rather than identifying irregularities, so this is not its primary function.

Extraction of insights from unstructured text data - While generative AI can work with unstructured data to produce new content, the extraction of insights from text generally refers to natural language processing (NLP) techniques like information retrieval, summarization, or sentiment analysis. Generative AI is focused on content creation, not the extraction of information from text.