Artificial Intelligence Flashcards
7 cards from real Microsoft Azure AI Fundamentals practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Artificial Intelligence flashcards as text
Which term describes the phenomenon where a generative AI model produces confident but factually incorrect or fabricated information?
Answer: Hallucination
Hallucination occurs when a generative AI model generates plausible-sounding but factually incorrect or entirely invented content.
A model trained on data from 2020 is used in 2026 without retraining. Which issue is most likely to affect its accuracy?
Answer: Model drift due to data distribution changes over time
Model drift (or data drift) occurs when real-world data patterns change over time, causing a model trained on older data to become less accurate.
In the context of Azure AI services, what is the purpose of an API key?
Answer: To authenticate requests to an Azure AI service endpoint
An API key authenticates API calls to Azure AI services, ensuring only authorized applications can access the resource.
Which responsible AI principle requires that people and organizations be answerable for the decisions and actions of AI systems?
Answer: Accountability
Accountability means humans and organizations must take responsibility for AI system outcomes and have processes to address harms.
What is the difference between artificial intelligence and machine learning?
Answer: AI is the broader field of creating intelligent systems; ML is a subset where systems learn from data
AI encompasses all techniques for creating intelligent behavior in machines, while ML is a specific AI approach where models learn patterns from data.
Which Azure service is specifically designed to detect, assess, and mitigate harmful content in AI applications?
Answer: Azure Content Safety
Azure Content Safety detects harmful or inappropriate content such as hate speech, violence, and sexual content in text and images.
A development team wants to monitor their deployed Azure Machine Learning model for changes in prediction accuracy over time. Which concept describes what they are monitoring?
Answer: Model drift
Model drift monitoring tracks whether a deployed model's performance degrades over time as real-world data patterns evolve.