(AI-900) 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 (AI-900) flashcards as text
Which type of AI workload involves training a model on labeled examples and using it to predict the label for new, unseen data?
Answer: Supervised learning
Supervised learning uses labeled training data so the model can learn a mapping from inputs to outputs and generalize to new examples.
In Azure Cognitive Services Language, what does the 'key phrase extraction' feature return?
Answer: The most important phrases that summarize the content of a document
Key phrase extraction identifies and returns the main talking points of a text, useful for quickly summarizing large volumes of content.
What is 'overfitting' in machine learning?
Answer: The model learns the training data too well and generalizes poorly to new data
An overfitted model memorizes training data noise and details, causing it to perform worse on unseen data than on the data it was trained on.
Which Azure service enables you to add a conversational AI interface—such as a chatbot—to websites, apps, or messaging channels?
Answer: Azure Bot Service
Azure Bot Service provides a managed platform for building, testing, deploying, and connecting intelligent bots to multiple communication channels.
What is the purpose of 'cross-validation' when evaluating a machine learning model?
Answer: To estimate model performance more robustly by training and evaluating on multiple data splits
Cross-validation partitions data into k folds, trains on k-1 folds, and tests on the remaining fold repeatedly, giving a more reliable performance estimate.
Which Responsible AI principle requires that AI systems be explainable so that humans can understand the decisions they make?
Answer: Transparency
The Transparency principle demands that AI systems and their decisions be understandable to stakeholders, enabling informed oversight and trust.
A company uses Azure Cognitive Search to build an internal knowledge base. What AI enrichment capability can automatically extract entities from documents during indexing?
Answer: AI enrichment skills (Skillsets)
AI enrichment skillsets in Azure Cognitive Search apply cognitive skills—such as entity recognition and key phrase extraction—to documents during the indexing pipeline.