- A
Key phrase extraction and sentiment analysis
Why wrong: Key phrase extraction identifies important phrases but not specific clauses or structured entities like monetary amounts. Sentiment analysis gauges emotion, not relevant.
- B
Custom text classification and named entity recognition
Custom text classification can identify which clauses are present (e.g., confidentiality). Named entity recognition can extract parties and monetary amounts. Together they fulfill both requirements.
- C
Entity linking and language detection
Why wrong: Entity linking associates entities with a knowledge base, not suitable for classifying clauses. Language detection identifies the language, not relevant.
- D
Summarization and conversational language understanding
Why wrong: Summarization creates a summary but does not extract specific clauses or entities. CLU is for intent recognition in chatbots, not for contract analysis.
Quick Answer
The answer is to use Custom Text Classification and Named Entity Recognition (NER) together. Custom text classification allows you to train a model on labeled examples to identify specific legal clauses like 'confidentiality' or 'indemnity', while NER automatically extracts predefined entities such as party names and monetary amounts from the contract text. On the AI-900 exam, this scenario tests your understanding of how Azure AI Language’s prebuilt and custom features complement each other—a common trap is thinking one feature can do both, but classification handles custom categories and NER handles standard entities. For a memory tip, remember that classification sorts documents into custom buckets (clauses), while NER plucks out specific data points (names and numbers).
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 process thousands of legal contracts. They want to extract specific clauses (e.g., 'confidentiality', 'indemnity') and also identify the names of parties and monetary amounts mentioned. Which Azure AI Language feature(s) should they use together to achieve both tasks?
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 named entity recognition
Custom text classification allows the law firm to train a model to identify specific clauses like 'confidentiality' and 'indemnity' by providing labeled examples. Named entity recognition (NER) can then extract predefined entities such as person names (parties) and monetary amounts from the text. Together, these two features address both custom clause detection and standard entity extraction.
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.
- ✗
Key phrase extraction and sentiment analysis
Why it's wrong here
Key phrase extraction identifies important phrases but not specific clauses or structured entities like monetary amounts. Sentiment analysis gauges emotion, not relevant.
- ✓
Custom text classification and named entity recognition
Why this is correct
Custom text classification can identify which clauses are present (e.g., confidentiality). Named entity recognition can extract parties and monetary amounts. Together they fulfill both requirements.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Entity linking and language detection
Why it's wrong here
Entity linking associates entities with a knowledge base, not suitable for classifying clauses. Language detection identifies the language, not relevant.
- ✗
Summarization and conversational language understanding
Why it's wrong here
Summarization creates a summary but does not extract specific clauses or entities. CLU is for intent recognition in chatbots, not for contract analysis.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think prebuilt NER alone can handle custom clauses, but NER only recognizes a fixed set of entity types (e.g., person, organization, money) and cannot identify domain-specific clauses without custom training.
Trap categories for this question
Keyword trap
Key phrase extraction identifies important phrases but not specific clauses or structured entities like monetary amounts. Sentiment analysis gauges emotion, not relevant.
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, enabling extraction of domain-specific clauses that are not part of the prebuilt NER categories. Prebuilt NER can recognize entities like 'Person' and 'Money' using a fixed taxonomy (e.g., ISO 4217 currency codes), but it cannot identify custom legal clauses without training. In practice, a law firm would first use custom text classification to tag clause types, then apply NER to extract parties and amounts from the classified sections, often chaining the two APIs in a pipeline.
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
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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 named entity recognition — Custom text classification allows the law firm to train a model to identify specific clauses like 'confidentiality' and 'indemnity' by providing labeled examples. Named entity recognition (NER) can then extract predefined entities such as person names (parties) and monetary amounts from the text. Together, these two features address both custom clause detection and standard entity extraction.
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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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.
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