Microsoft Azure AI Fundamentals 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 flashcards as text
Which Azure AI capability would you use to enable a call center application to automatically convert customer speech into text for analysis?
Answer: Speech-to-Text (Speech Recognition)
Speech-to-Text transcribes spoken audio into written text in real time, making it ideal for call center transcription and analysis.
What is 'transfer learning' in machine learning?
Answer: Applying knowledge learned from one task to improve performance on a different but related task
Transfer learning reuses a pre-trained model's learned representations as a starting point for a new task, reducing training time and data requirements.
Which responsible AI principle focuses on ensuring that AI system decisions can be understood and explained to users?
Answer: Transparency
Transparency means AI systems should be understandable — users and stakeholders should be able to see how and why decisions were made.
What is the purpose of the 'Content Moderator' service in Azure Cognitive Services?
Answer: To detect potentially offensive, risky, or unwanted content in text and images
Azure Content Moderator scans text, images, and video for explicit, offensive, or otherwise policy-violating content to support safe online platforms.
In Azure Machine Learning, what is the difference between 'inference' and 'training'?
Answer: Training builds the model by learning from data; inference uses the trained model to make predictions
Training is the process of fitting a model to data to learn patterns, while inference is using that trained model to generate predictions on new input data.
Which Azure OpenAI model capability allows it to understand and respond to both text and images in the same prompt?
Answer: Multi-modal (vision) capability
Multi-modal models like GPT-4o can accept both text and image inputs, enabling use cases like image description, visual Q&A, and document analysis.
What does 'AutoML' in Azure Machine Learning automatically handle for the user?
Answer: Iterating through algorithms and hyperparameters to find the best model for a dataset
AutoML automates the process of algorithm selection, feature engineering, and hyperparameter tuning to find the best-performing model with minimal manual effort.