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
A hospital wants to automatically extract patient symptoms and medication names from clinical notes. They have a set of pre-defined categories for symptoms and medications, and they have manually labeled a few hundred sentences to indicate which text spans belong to each category. Which Azure AI Language feature should they use to build this custom entity extraction solution?
⚠ Common exam trap
Candidates often confuse 'custom text classification' (which labels whole documents) with 'custom entity extraction' (which labels specific spans), leading them to pick option C when the question explicitly asks for extracting text spans, not classifying entire sentences.
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
✓
Custom entity extraction
Custom entity extraction (D) is the correct choice because the hospital needs to identify specific text spans (symptoms and medication names) based on their own pre-defined categories, using a small set of manually labeled sentences for training. This is exactly what Azure's custom named entity recognition (NER) feature does—it allows you to train a model to extract custom entities from unstructured text, tailored to your domain-specific labels.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Pre-built entity recognition
Why it's wrong here
Pre-built entity recognition in Azure AI Language applies a fixed, general-purpose model that recognizes common types like people, dates, and locations. Because it is not trainable, it cannot be adapted to identify medical concepts such as 'symptom' as a custom entity type. It might incidentally find phrases like 'fever', but it doesn't label them in a hospital-specific taxonomy. Therefore, it fails to meet the requirement for extracting symptoms in a tailored way.
- ✗
Key phrase extraction
Why it's wrong here
Key phrase extraction performs an unsupervised statistical analysis of the text to select words or short phrases that appear most salient. It returns a list of key phrases without any semantic classification or entity tagging, so it never tells you that a phrase represents a 'symptom' versus a 'procedure' or 'medication'. Even if it surfaces 'chest pain', it provides no way to train the model or define custom categories, making it unsuitable for structured symptom extraction.
- ✗
Custom text classification
Why it's wrong here
Custom text classification, whether single-label or multi-label, assigns a category to an entire document or a sentence, not to individual text spans. You could train it to classify whole notes as 'contains symptom X', but it doesn't locate and extract the exact words or phrases that constitute each symptom. It doesn't generate entities or support span-level annotations, so it cannot deliver the structured symptom spans the hospital needs.
- ✓
Custom entity extraction
Why this is correct
Custom entity extraction (custom NER) in Azure AI Language lets you define your own entity types, such as 'symptom' or 'medication', and tag labeled examples in clinical notes. You then train a model that learns to recognize and extract those specialized entities from unseen text, including varied phrasing and abbreviations. Because you control the labels and training data, you can precisely extract each symptom occurrence, making it the correct choice for this scenario.
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Azure Machine Learning Studio
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
Key term
Named entity recognition
Named entity recognition (NER) is an Azure AI service feature that automatically identifies and classifies key pieces of information in text, such as names of people, organizations, locations, dates, and other specific data.
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