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
What distinguishes deep learning from traditional machine learning?
Answer: Deep learning uses neural networks with multiple layers to learn hierarchical feature representations
Deep learning employs neural networks with many hidden layers that automatically learn increasingly abstract feature representations from raw data.
Which Azure service allows you to create a custom speech recognition model tailored to your organization's specific vocabulary?
Answer: Azure Custom Speech (part of Speech Services)
Azure Custom Speech lets you upload domain-specific audio and transcripts to train a custom speech model that recognizes specialized terminology.
A retail company wants to recommend products to customers based on the purchases of similar customers. Which AI approach is this?
Answer: Collaborative filtering (recommendation)
Collaborative filtering recommends items by finding users with similar behavior patterns and suggesting items those similar users have interacted with.
What is the role of 'features' in a machine learning model?
Answer: They are the input variables used to make predictions
Features are the measurable input variables or attributes provided to a machine learning model to help it learn patterns and make predictions.
Which Azure service enables developers to add conversational AI capabilities to applications using pre-built and custom dialog flows?
Answer: Azure Bot Service
Azure Bot Service provides a framework for building, testing, and deploying intelligent bots with conversational interfaces across multiple channels.
In Azure Custom Vision, what is the minimum recommended number of images per tag for a good starting model?
Answer: 15 images
Azure Custom Vision recommends at least 15 images per tag as a starting point, though more images generally lead to better model accuracy.
Which concept describes the process of using a model trained on one task as the starting point for a different but related task?
Answer: Transfer learning
Transfer learning reuses knowledge gained from training on one problem to improve learning efficiency and performance on a related problem.