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 Azure AI service can analyze video content to extract insights such as scene detection, speaker identification, and transcript generation?
Answer: Azure Video Indexer
Azure Video Indexer uses AI to automatically extract rich insights from video, including transcripts, faces, emotions, topics, and scene changes.
What is 'data drift' in the context of deployed machine learning models?
Answer: Changes in input data distribution over time that degrade model performance
Data drift occurs when the statistical properties of production data change over time compared to the training data, causing model accuracy to decline.
Which of the following best describes 'natural language generation' (NLG)?
Answer: Converting structured data or prompts into human-readable text
NLG is the AI capability of producing coherent, human-readable text from structured data, templates, or learned patterns.
In Azure Machine Learning, what is a 'pipeline'?
Answer: A reusable workflow of ML steps such as data prep, training, and evaluation
An Azure ML pipeline is a reusable, automated sequence of steps that defines and orchestrates an end-to-end machine learning workflow.
Which responsible AI principle is most directly concerned with preventing AI systems from producing biased outcomes against specific demographic groups?
Answer: Fairness
Fairness requires that AI systems treat all individuals and groups equitably and do not produce discriminatory outcomes based on characteristics like race or gender.
What type of AI task is performed when an Azure service listens to spoken words and converts them into written text?
Answer: Speech-to-text transcription
Speech-to-text (also called automatic speech recognition or ASR) converts spoken audio input into written text output.
Which Azure Machine Learning feature helps explain why a model made a specific prediction by showing feature importance?
Answer: Model Interpretability / Explainability
Azure ML's model interpretability tools, including SHAP and LIME-based explanations, reveal which input features most influenced a specific prediction.