Describe features of Natural Language Processing workloads on Azure →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
What is 'multi-label text classification' vs 'single-label' in Azure AI Language?
⚠ Common exam trap
Watch out — candidates often confuse 'multi-label' with 'multi-class' (which still assigns only one label per document) or assume multi-label is always better, ignoring that it requires different model architecture and training data.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Single-label assigns exactly one category; multi-label allows multiple categories per document
In Azure AI Language, single-label text classification assigns exactly one category to each document, while multi-label classification allows a document to be assigned multiple categories simultaneously. This distinction is fundamental to how the classification models are trained and how predictions are structured, with multi-label using a separate binary classifier per label rather than a single softmax output.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Single-label classifies one word; multi-label classifies entire sentences
Why it's wrong here
This option misstates the input granularity: both single-label and multi-label models in Azure AI Language operate on a document or text snippet as a whole, not on individual words. A phrase like 'predict next word' is a token-level task, not what document classification does. The true contrast is whether the classifier must choose exactly one category or can output multiple categories for the same input text.
- ✓
Single-label assigns exactly one category; multi-label allows multiple categories per document
Why this is correct
Single-label classification assigns exactly one mutually exclusive category to each document, such as routing a support ticket to either 'billing' or 'technical'. Multi-label classification allows the same document to receive multiple non-exclusive labels simultaneously, e.g., tagging an article as both 'politics' and 'economy'. The key distinction is the number of labels that can be applied, not the structure of the text being classified.
- ✗
Multi-label is more accurate because it considers more information per document
Why it's wrong here
Accuracy is not determined by the number of labels per document; multi-label classification assigns zero or more non-mutually-exclusive labels to a single document, whereas single-label assigns exactly one label from a set. The temptation arises because multi-label does capture richer document semantics, and it would be the correct choice when a document genuinely belongs to multiple categories simultaneously, such as tagging a news article as both "politics" and "economy".
- ✗
Single-label requires more training data than multi-label classification
Why it's wrong here
Training data requirements are not systematically determined by whether the classification mode is single-label or multi-label. They depend instead on the number of categories, how imbalanced the class distribution is, the ambiguity of documents, and the complexity of the features. In practice, multi-label annotation can be more demanding because each document may need several correct labels validated, whereas single-label examples need only one.
Go deeper
Related to this question
Learn chapter
Regression and Classification
Key term
Classification
Classification is a supervised machine learning technique used to predict a category or class label for new data based on patterns learned from labeled training data.
Key term
Azure AI Language
Azure AI Language is a cloud-based service from Microsoft that uses natural language processing to understand, analyze, and generate human language for applications.
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