- A
Sentiment analysis and key phrase extraction
Why wrong: Sentiment analysis detects positive/negative tone, and key phrase extraction finds important words — neither assigns department categories nor extracts specific custom entities.
- B
Language detection and translation
Why wrong: Language detection identifies the language of the email and translation changes it to another language; these do not route or extract custom data.
- C
Text classification and custom entity recognition
Text classification (custom) categorizes emails into departments, and custom entity recognition extracts organization-specific fields like account numbers and order IDs.
- D
Named entity recognition (NER) and summarization
Why wrong: 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.
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.
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 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?
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
Option C is correct because 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.
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 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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Trap categories for this question
Keyword trap
Sentiment analysis detects positive/negative tone, and key phrase extraction finds important words — neither assigns department categories nor extracts specific custom entities.
Detailed technical explanation
How to think about this question
Custom text classification in Azure AI Language uses a trained model to assign one or more labels (e.g., 'Billing') to input text, while custom entity recognition (NER) uses a trained model to identify and extract user-defined entities such as 'AccountNumber' or 'OrderID' from unstructured text. Under the hood, both features leverage transformer-based models fine-tuned on user-provided labeled data, and the extraction of custom entities can handle patterns like alphanumeric sequences (e.g., 'ORD-12345') that prebuilt NER would miss.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Describe features of Natural Language Processing workloads on Azure — study guide chapter
Learn the concepts, then practise the questions
- →
Describe features of Natural Language Processing workloads on Azure practice questions
Targeted practice on this topic area only
- →
All AI-900 questions
1,020 questions across all exam domains
- →
Microsoft Azure AI Fundamentals AI-900 study guide
Full concept coverage aligned to exam objectives
- →
AI-900 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Describe Artificial Intelligence workloads and considerations practice questions
Practise AI-900 questions linked to Describe Artificial Intelligence workloads and considerations.
Describe fundamental principles of machine learning on Azure practice questions
Practise AI-900 questions linked to Describe fundamental principles of machine learning on Azure.
Describe features of computer vision workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of computer vision workloads on Azure.
Describe features of Natural Language Processing workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of Natural Language Processing workloads on Azure.
Describe features of generative AI workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of generative AI workloads on Azure.
AI-900 fundamentals practice questions
Practise AI-900 questions linked to AI-900 fundamentals.
AI-900 scenario practice questions
Practise AI-900 questions linked to AI-900 scenario.
AI-900 troubleshooting practice questions
Practise AI-900 questions linked to AI-900 troubleshooting.
Practice this exam
Start a free AI-900 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this AI-900 question test?
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: Text classification and custom entity recognition — Option C is correct because 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.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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: Option B is correct because 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.
Last reviewed: Jun 11, 2026
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.