Microsoft Azure AI Fundamentals MCQ 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 Microsoft Azure AI Fundamentals MCQ flashcards as text
Which metric would be most appropriate for evaluating a binary classification model predicting rare fraud events?
Answer: Precision-Recall AUC
For imbalanced datasets like fraud detection, Precision-Recall AUC is more informative than accuracy, which can be misleadingly high when the positive class is rare.
Azure Cognitive Services Language Understanding (LUIS) is primarily used for:
Answer: Extracting intents and entities from natural language input
LUIS analyzes natural language to identify the user's intent and extract relevant entities, enabling conversational AI applications.
Which Azure service is specifically designed to build, train, and deploy machine learning models at scale?
Answer: Azure Machine Learning
Azure Machine Learning is a cloud platform designed to support the full ML lifecycle from data preparation to model training, deployment, and monitoring.
What is a 'confusion matrix' used for in machine learning?
Answer: Evaluating classification model performance by showing true vs predicted labels
A confusion matrix shows the counts of true positives, true negatives, false positives, and false negatives, providing a comprehensive view of classifier performance.
Which Azure AI capability can identify the specific objects present in an image along with their locations?
Answer: Object detection
Object detection not only identifies what objects are in an image but also provides bounding boxes showing where each object is located.
In the context of Azure AI, what is a 'knowledge base' in Azure QnA Maker (now part of Azure AI Language)?
Answer: A structured collection of question-and-answer pairs used by a chatbot
A knowledge base in QnA Maker is a curated set of Q&A pairs that enables a chatbot to answer user questions by matching them to stored answers.
Which principle of responsible AI focuses on ensuring AI systems work correctly and safely across various conditions?
Answer: Reliability and safety
Reliability and safety ensures AI systems perform as designed under normal and adverse conditions without causing unintended harm.