Describe features of Natural Language Processing workloads on Azure →hardMultiple ChoiceObjective-mapped
Which Azure AI Language Features Route Emails and Extract Account Numbers?
A customer support team receives thousands of emails daily. They want to automatically route each email to the appropriate department (Billing, Technical Support, or General Inquiry). They also want to extract the customer's account number and order ID from each email. Which combination of Azure AI Language features should they use?
Quick Answer
The correct answer is custom text classification paired with custom entity recognition. This combination directly addresses the two distinct natural language processing tasks in the scenario: categorizing emails into Billing, Technical Support, or General Inquiry requires text classification, while extracting account numbers and order IDs demands custom entity recognition to identify domain-specific data that prebuilt named entity recognition cannot capture. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your understanding of how Azure AI Language features map to real-world business problems, often presenting a trap where learners mistakenly choose prebuilt NER for extraction or sentiment analysis for routing. Remember the memory tip: “Classify the bucket, extract the detail”—text classification sorts items into categories, while custom entity recognition pulls out specific, structured values unique to your organization.
⚠ Common exam trap
Watch out — candidates often confuse prebuilt named entity recognition (NER) with custom entity recognition, assuming NER can extract any entity type, when in fact NER only handles a fixed set of common categories and cannot extract domain-specific fields like account numbers or order IDs without custom training.
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
✓
Text classification and custom entity recognition
The scenario requires two distinct NLP tasks: categorizing emails into predefined departments (Billing, Technical Support, General Inquiry) which is a text classification task, and extracting specific structured data (account number and order ID) which requires custom entity recognition to identify domain-specific entities not covered by prebuilt NER. Azure AI Language provides both custom text classification and custom entity recognition features to handle these requirements.
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 and key phrase extraction
Why it's wrong here
Sentiment analysis detects positive/negative tone, and key phrase extraction finds important words — neither assigns department categories nor extracts specific custom entities.
- ✗
Language detection and translation
Why it's wrong here
Language detection identifies the language of the email and translation changes it to another language; these do not route or extract custom data.
- ✓
Text classification and custom entity recognition
Why this is correct
Text classification (custom) categorizes emails into departments, and custom entity recognition extracts organization-specific fields like account numbers and order IDs.
- ✗
Named entity recognition (NER) and summarization
Why it's wrong here
Pre-built NER extracts general entities (e.g., people, places) but not custom fields like account numbers; summarization condenses text without extracting specific data or classifying.
Go deeper
Related to this question
Learn chapter
Regression and Classification
Key term
NLP
NLP (Natural Language Processing) is a branch of artificial intelligence that enables computers to understand, interpret, and generate human language in a way that is meaningful and useful.
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.
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Same concept, more angles
3 more ways this is tested on AI-900
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 customer service organization has thousands of support tickets labeled with predefined categories such as 'Billing', 'Technical', and 'Account Management'. They want to build a solution that automatically assigns a category to new, incoming tickets. The categories are fixed and known in advance. Which Azure AI Language service feature should they use?
hard- A.Prebuilt Text Analytics
- ✓ B.Custom Text Classification
- C.Language Understanding (LUIS)
- D.Translator
Why B: Custom Text Classification (B) is correct because the organization has a fixed set of predefined categories and needs to classify new support tickets into those categories. This feature allows you to train a custom model using labeled examples of 'Billing', 'Technical', and 'Account Management' tickets, enabling automatic assignment of incoming tickets to the correct category.
Variation 2. A law firm receives hundreds of legal documents daily. They want to use Azure AI Language to automatically assign each document to exactly one predefined category, such as 'Contract', 'Trademark', or 'Litigation'. Which Azure AI Language feature is specifically designed for this task?
medium- A.Named Entity Recognition (NER)
- B.Key phrase extraction
- ✓ C.Custom text classification (single-label)
- D.Conversational Language Understanding (CLU)
Why C: Custom text classification (single-label) is the correct feature because it allows you to train a model to assign each document to exactly one predefined category (e.g., 'Contract', 'Trademark', 'Litigation') based on your own labeled data. This is distinct from prebuilt features like NER or key phrase extraction, which do not perform document-level categorization into custom classes.
Variation 3. A law firm receives hundreds of legal documents daily. They need to automatically extract key entities like names of parties, dates, jurisdictions, and also classify each document as 'contract', 'pleading', or 'memo'. Which combination of Azure AI Language features should they use?
medium- A.Entity recognition and key phrase extraction
- ✓ B.Entity recognition and custom text classification
- C.Sentiment analysis and language detection
- D.Summarization and conversation analysis
Why B: The law firm needs both entity extraction (to identify parties, dates, jurisdictions) and document classification (contract, pleading, memo). Azure AI Language's prebuilt entity recognition handles the entity extraction, while custom text classification allows the firm to train a model to classify documents into their specific categories. This combination directly addresses both requirements without unnecessary features.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.