The answer is custom text classification, because it enables supervised learning where a model is trained on labeled examples to sort text into predefined categories. In this scenario, the legal firm’s 500 manually labeled documents serve as the training data, allowing the Azure AI Language feature to learn the distinguishing patterns of ‘Contract’, ‘Brief’, ‘Motion’, and ‘Discovery’ and then automatically classify new incoming documents. On the AI-900 exam, this question tests your understanding of the difference between built-in text classification (which uses pre-trained models for general categories like sentiment) and custom text classification (which requires your own labeled data). A common trap is confusing custom text classification with the pre-built document analysis features; remember that if you see “labeled examples” or “your own categories,” the answer is always custom text classification. Memory tip: “Custom needs your corpus”—if you have a corpus of labeled documents, you need the custom feature.
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 legal firm needs to automatically sort incoming legal documents into predefined categories such as 'Contract', 'Brief', 'Motion', and 'Discovery'. They have a set of 500 manually labeled documents to use as examples. Which Azure AI Language feature should they use to build this classification system?
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 is the correct choice because it allows the legal firm to train a model using their 500 labeled documents to classify text into predefined categories like 'Contract', 'Brief', 'Motion', and 'Discovery'. This feature enables supervised learning where the model learns from labeled examples to automatically sort incoming documents, which is exactly the requirement described.
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.
✓
Custom text classification
Why this is correct
Custom text classification is designed to classify documents into user-defined categories using labeled examples, perfect for this scenario.
Related concept
Read the scenario before looking for a memorised answer.
✗
Key phrase extraction
Why it's wrong here
Key phrase extraction identifies important phrases in text but does not classify documents into your own categories.
✗
Named entity recognition
Why it's wrong here
Named entity recognition extracts predefined entities like persons, dates, and organizations, not custom categories.
✗
Sentiment analysis
Why it's wrong here
Sentiment analysis determines the emotional tone of text, not the document category.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse custom text classification with built-in features like key phrase extraction or named entity recognition, mistakenly thinking those can perform document-level categorization when they are designed for different NLP tasks.
Trap categories for this question
Keyword trap
Key phrase extraction identifies important phrases in text but does not classify documents into your own categories.
Detailed technical explanation
How to think about this question
Custom text classification in Azure AI Language uses a transformer-based model fine-tuned on the user's labeled dataset via a training pipeline that splits data into training and validation sets. The service supports both single-label and multi-label classification, and the model's performance can be evaluated using metrics like precision, recall, and F1 score. In a real-world scenario, the legal firm would need to ensure their 500 documents are representative and balanced across categories to avoid bias and achieve high accuracy.
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 is the correct choice because it allows the legal firm to train a model using their 500 labeled documents to classify text into predefined categories like 'Contract', 'Brief', 'Motion', and 'Discovery'. This feature enables supervised learning where the model learns from labeled examples to automatically sort incoming documents, which is exactly the requirement described.
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.
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Question Discussion
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