General 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 General flashcards as text
Which Azure service allows developers to add pre-built AI capabilities to applications without needing machine learning expertise?
Answer: Azure AI Services (Cognitive Services)
Azure AI Services (formerly Cognitive Services) offers pre-built APIs for vision, language, speech, and decision tasks that developers can integrate without ML expertise.
What is 'overfitting' in a machine learning model?
Answer: The model performs well on training data but poorly on new, unseen data
Overfitting occurs when a model memorizes training data so well that it fails to generalize to new data, resulting in low training error but high validation error.
In Azure AI Vision, which feature identifies the age, gender, and emotion of people in photographs?
Answer: Face detection and analysis
Azure AI Vision's face detection and analysis feature can locate faces in images and return attributes such as estimated age, inferred gender, and detected emotion.
Which Azure feature enables you to detect anomalies in time-series data such as server metrics or sensor readings?
Answer: Azure Anomaly Detector
Azure Anomaly Detector is an AI service that automatically identifies unexpected patterns in time-series data without requiring labeled training data.
What does the term 'neural network' refer to in artificial intelligence?
Answer: A computational model inspired by the structure of the human brain
A neural network is a computational model made up of interconnected nodes (neurons) organized in layers, loosely inspired by the biological structure of the human brain.
Which Azure AI service can extract structured data from forms, invoices, and receipts?
Answer: Azure AI Document Intelligence
Azure AI Document Intelligence (formerly Form Recognizer) uses AI to extract structured fields, tables, and key-value pairs from documents like invoices and receipts.
What is the primary goal of 'feature engineering' in a machine learning workflow?
Answer: Transforming raw data into informative input variables that improve model performance
Feature engineering involves creating, selecting, and transforming variables from raw data to provide the model with more informative inputs that improve predictive accuracy.