(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 Azure Machine Learning feature automatically tries multiple algorithms and hyperparameter combinations to find the best model for a dataset?
Answer: Automated ML (AutoML)
Automated ML iterates through algorithm and feature preprocessing combinations, evaluating each using cross-validation to surface the best-performing model.
What is 'responsible AI' most accurately described as?
Answer: A framework of principles guiding the ethical and trustworthy development and use of AI
Responsible AI is a set of guiding principles—including fairness, reliability, privacy, inclusiveness, transparency, and accountability—for building trustworthy AI systems.
In natural language processing, what does 'tokenization' refer to?
Answer: Splitting text into individual units such as words or subwords for processing
Tokenization breaks raw text into tokens (words, subwords, or characters) that serve as the basic units for downstream NLP processing.
Which Azure service would you use to translate text between more than 100 languages in real time?
Answer: Azure Translator
Azure Translator is a cloud-based neural machine translation service that supports text translation, transliteration, and language detection across 100+ languages.
What is a 'confusion matrix' used for in machine learning evaluation?
Answer: Showing the counts of correct and incorrect predictions broken down by class
A confusion matrix tabulates true positives, true negatives, false positives, and false negatives, giving a detailed view of classification errors.
Which component of an Azure ML workspace stores the versioned datasets, models, and experiment run history?
Answer: Datastore and registry artifacts
Datastores link to Azure storage accounts to hold datasets, while the workspace registry tracks versioned models and experiment artifacts for reproducibility.
A hospital wants to predict which patients are at high risk for readmission within 30 days. What type of machine learning problem is this?
Answer: Binary classification
Predicting whether a patient will be readmitted within 30 days is a binary classification task with two outcomes: readmitted or not readmitted.