The answer is Named Entity Recognition (NER) and Custom Text Classification. NER is the correct choice for extracting specific entities like party names, court names, and filing dates because it automatically identifies and categorizes predefined entities within unstructured text, making it ideal for pulling structured data from legal documents. Custom Text Classification, on the other hand, is the right tool for categorizing each document into custom labels like 'complaint', 'motion', or 'subpoena' because it allows you to train a model on your own labeled examples, handling domain-specific categories that prebuilt models cannot cover. On the AI-900 exam, this question tests your ability to distinguish between Azure AI Language’s prebuilt features (NER) and customizable features (Custom Text Classification), a common trap where candidates confuse entity extraction with document categorization. Remember the memory tip: “NER pulls the who, what, and when; Custom Text Classification sorts the document into a bin.”
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
This AI-900 practice question tests your understanding of describe features of natural language processing workloads on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A law firm needs to automatically process incoming legal documents. They have two specific requirements: (1) extract the names of all parties involved, the court name, and the filing date; (2) categorize each document as a 'complaint', 'motion', or 'subpoena'. Which two Azure AI Language features should they use? (Choose two.)
Why wrong: Sentiment analysis determines the emotional tone of text, not entity extraction or document categorization.
B
Key phrase extraction
Why wrong: Key phrase extraction identifies important phrases but does not extract specific structured entities or classify documents into custom categories.
C
Custom text classification
Custom text classification can be trained to assign user-defined labels such as 'complaint', 'motion', or 'subpoena' to documents.
D
Named entity recognition (NER)
NER extracts specific entities like names of parties, court names, and dates from unstructured text, fulfilling the extraction requirement.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Custom text classification
Custom text classification (C) is correct because it allows the law firm to train a model to categorize legal documents into custom classes like 'complaint', 'motion', or 'subpoena' based on labeled examples. This feature is designed for domain-specific classification tasks where predefined categories are insufficient.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Sentiment analysis
Why it's wrong here
Sentiment analysis determines the emotional tone of text, not entity extraction or document categorization.
✗
Key phrase extraction
Why it's wrong here
Key phrase extraction identifies important phrases but does not extract specific structured entities or classify documents into custom categories.
✓
Custom text classification
Why this is correct
Custom text classification can be trained to assign user-defined labels such as 'complaint', 'motion', or 'subpoena' to documents.
Related concept
Read the scenario before looking for a memorised answer.
✓
Named entity recognition (NER)
Why this is correct
NER extracts specific entities like names of parties, court names, and dates from unstructured text, fulfilling the extraction requirement.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse key phrase extraction with named entity recognition, but key phrase extraction returns untyped phrases rather than structured entities with predefined categories, and it cannot perform document-level classification.
Trap categories for this question
Keyword trap
Key phrase extraction identifies important phrases but does not extract specific structured entities or classify documents into custom categories.
Detailed technical explanation
How to think about this question
Named entity recognition (NER) uses a pre-trained model to identify and categorize entities such as persons, organizations, and dates from text, making it ideal for extracting party names, court names, and filing dates. Custom text classification, on the other hand, requires a training dataset with labeled documents and uses a multi-class classification algorithm to assign each document to one of the defined categories, enabling the law firm to handle domain-specific legal document types.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Describe features of Natural Language Processing workloads on Azure — This question tests Describe features of Natural Language Processing workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Custom text classification — Custom text classification (C) is correct because it allows the law firm to train a model to categorize legal documents into custom classes like 'complaint', 'motion', or 'subpoena' based on labeled examples. This feature is designed for domain-specific classification tasks where predefined categories are insufficient.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. 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?
medium
A.Pre-built entity recognition
B.Key phrase extraction
C.Custom text classification
✓ D.Custom entity extraction
Why D: 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.
Last reviewed: Jun 11, 2026
Question Discussion
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