- A
Prebuilt sentiment analysis and key phrase extraction
Why wrong: These prebuilt features analyze sentiment and extract generic key phrases, not custom categories or specific clauses.
- B
Custom text classification and custom named entity recognition
Custom text classification allows training on labeled categories, and custom NER allows extracting user-defined entities like specific clauses.
- C
Question answering and conversation summarization
Why wrong: Question answering answers queries from a knowledge base, and conversation summarization creates summaries; neither categorizes documents or extracts custom entities.
- D
Language detection and translation
Why wrong: Language detection identifies the language, and translation converts text; they do not classify or extract custom information.
Quick Answer
The correct answer is Custom text classification and custom named entity recognition (NER) because the law firm’s dual need—sorting documents into categories like 'contract' and simultaneously extracting specific clauses such as 'indemnity'—requires two distinct but complementary Azure AI Language features. Custom text classification handles the document-level categorization by learning from labeled examples, while custom NER pinpoints and labels domain-specific phrases within the text, making the combination ideal for this scenario. On the AI-900 exam, this question tests your understanding of when to use built-in versus custom features; a common trap is choosing prebuilt models, which lack the flexibility to handle specialized legal terminology. Remember the pairing: classification sorts the whole document, NER extracts the parts. A useful memory tip is “Classify the file, extract the clause”—if you need both, you need both custom features.
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 categorize documents (e.g., 'contract', 'pleading', 'memo') and extract specific clauses such as 'indemnity' and 'confidentiality'. They have a large set of labeled examples for both tasks. 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
Custom text classification and custom named entity recognition
Option B is correct because the law firm needs to categorize documents (a text classification task) and extract specific clauses (a named entity recognition task). Custom text classification allows training a model on labeled examples to classify documents into categories like 'contract' or 'pleading', while custom named entity recognition (NER) can be trained to extract domain-specific entities such as 'indemnity' and 'confidentiality' clauses from the text. Azure AI Language supports both custom features, enabling the firm to build tailored models using their labeled dataset.
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.
- ✗
Prebuilt sentiment analysis and key phrase extraction
Why it's wrong here
These prebuilt features analyze sentiment and extract generic key phrases, not custom categories or specific clauses.
- ✓
Custom text classification and custom named entity recognition
Why this is correct
Custom text classification allows training on labeled categories, and custom NER allows extracting user-defined entities like specific clauses.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Question answering and conversation summarization
Why it's wrong here
Question answering answers queries from a knowledge base, and conversation summarization creates summaries; neither categorizes documents or extracts custom entities.
- ✗
Language detection and translation
Why it's wrong here
Language detection identifies the language, and translation converts text; they do not classify or extract custom information.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse prebuilt features (like sentiment analysis or key phrase extraction) with custom features, assuming that prebuilt models can be adapted to domain-specific tasks without training, when in fact only custom text classification and custom NER can leverage labeled examples for tailored document categorization and entity extraction.
Trap categories for this question
Keyword trap
These prebuilt features analyze sentiment and extract generic key phrases, not custom categories or specific clauses.
Detailed technical explanation
How to think about this question
Custom text classification in Azure AI Language uses a transformer-based model fine-tuned on user-provided labeled data, supporting both single-label and multi-label classification. Custom NER leverages a similar approach, allowing the model to learn entity boundaries and types from annotated spans, which is essential for extracting legal clauses that may vary in phrasing. Under the hood, both features use the same underlying API endpoint but require separate project types and training datasets, and they can be chained in a pipeline to first classify a document and then extract entities from the classified content.
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.
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: Custom text classification and custom named entity recognition — Option B is correct because the law firm needs to categorize documents (a text classification task) and extract specific clauses (a named entity recognition task). Custom text classification allows training a model on labeled examples to classify documents into categories like 'contract' or 'pleading', while custom named entity recognition (NER) can be trained to extract domain-specific entities such as 'indemnity' and 'confidentiality' clauses from the text. Azure AI Language supports both custom features, enabling the firm to build tailored models using their labeled dataset.
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 →
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.