The answer is Custom Named Entity Recognition (Custom NER) in Azure AI Language. This feature is correct because it enables the legal firm to train a custom model using their manually labeled contracts, allowing the system to learn and extract domain-specific entities like 'Party Name', 'Effective Date', and 'Governing Law' that are not covered by the predefined categories in built-in NER. On the Microsoft Azure AI Fundamentals AI-900 exam, this scenario tests your understanding of when to choose a custom AI feature over a prebuilt one—a common trap is selecting standard NER, which only recognizes generic entities like person or date. The key distinction is that Custom NER requires labeled training data to identify unique, industry-specific terms. For a quick memory tip, think of it this way: if the entities are not in the standard list, you need to “customize” the NER.
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 extract custom entities such as 'Party Name', 'Effective Date', and 'Governing Law' from contracts. They have a set of manually labeled contracts to use as training data. Which Azure AI Language feature should they use?
Why wrong: Key Phrase Extraction identifies the main points in text but does not extract specific custom entities.
B
Entity Linking
Why wrong: Entity Linking disambiguates entity mentions by linking them to a knowledge base (e.g., Wikipedia), not for custom entity extraction.
C
Custom Named Entity Recognition (Custom NER)
Correct. Custom NER allows training a model to extract specific custom entities using labeled data.
D
Built-in Named Entity Recognition
Why wrong: Built-in NER recognizes common predefined entities like people, organizations, and dates, but not custom entities like 'Party Name' or 'Governing Law'.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Custom Named Entity Recognition (Custom NER)
Custom Named Entity Recognition (Custom NER) is the correct choice because it allows the legal firm to train a model on their manually labeled contracts to extract domain-specific entities like 'Party Name', 'Effective Date', and 'Governing Law'. Unlike built-in NER, which only recognizes predefined entity types, Custom NER learns from the provided labeled data to identify custom categories tailored to the firm's needs.
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
Why it's wrong here
Key Phrase Extraction identifies the main points in text but does not extract specific custom entities.
✗
Entity Linking
Why it's wrong here
Entity Linking disambiguates entity mentions by linking them to a knowledge base (e.g., Wikipedia), not for custom entity extraction.
✓
Custom Named Entity Recognition (Custom NER)
Why this is correct
Correct. Custom NER allows training a model to extract specific custom entities using labeled data.
Related concept
Read the scenario before looking for a memorised answer.
✗
Built-in Named Entity Recognition
Why it's wrong here
Built-in NER recognizes common predefined entities like people, organizations, and dates, but not custom entities like 'Party Name' or 'Governing Law'.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Custom NER with Built-in NER, assuming the built-in version can be adapted to custom entities, but Built-in NER is fixed and cannot be retrained for domain-specific categories.
Trap categories for this question
Keyword trap
Key Phrase Extraction identifies the main points in text but does not extract specific custom entities.
Detailed technical explanation
How to think about this question
Custom NER in Azure AI Language uses a transformer-based model that is fine-tuned on the user's labeled dataset. The training process involves tokenizing the text, aligning labels, and optimizing the model to recognize custom entity boundaries and types. A key subtlety is that the model requires a minimum of 10 labeled instances per entity type for reliable performance, and it can handle overlapping entities through a span-based prediction mechanism.
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 Named Entity Recognition (Custom NER) — Custom Named Entity Recognition (Custom NER) is the correct choice because it allows the legal firm to train a model on their manually labeled contracts to extract domain-specific entities like 'Party Name', 'Effective Date', and 'Governing Law'. Unlike built-in NER, which only recognizes predefined entity types, Custom NER learns from the provided labeled data to identify custom categories tailored to the firm's needs.
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
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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 legal firm needs to automatically extract key information from contracts, including the names of parties involved, important dates, and monetary amounts. Which Azure AI Language feature should they use to identify and extract these specific pieces of information from the text?
medium
A.Sentiment analysis
B.Key phrase extraction
✓ C.Named Entity Recognition (NER)
D.Language detection
Why C: Named Entity Recognition (NER) is the correct Azure AI Language feature because it is specifically designed to identify and categorize entities such as people (parties involved), dates, and monetary amounts from unstructured text. This directly matches the legal firm's requirement to extract key information from contracts.
Variation 2. A legal firm needs to process thousands of contracts to automatically identify important terms such as dates, monetary amounts, names of parties, and legal citations. Which built-in feature of the Azure AI Language service is best suited for this task?
hard
A.A) Sentiment Analysis
B.B) Key Phrase Extraction
✓ C.C) Entity Recognition
D.D) Language Detection
Why C: Entity Recognition (also called Named Entity Recognition, NER) is the correct choice because it is specifically designed to identify and categorize predefined entities such as dates, monetary amounts, person names, organizations, and legal citations from unstructured text. The Azure AI Language service's NER capability can automatically extract these important terms from thousands of contracts, making it the ideal built-in feature for this task.
Variation 3. A legal department needs to automatically extract specific entities from contracts, such as 'Effective Date', 'Governing Law', and 'Payment Terms'. They have 500 manually labeled contract clauses that specify which text spans correspond to each entity. Which Azure AI Language feature should they use to build this custom entity extraction solution?
medium
A.Prebuilt Named Entity Recognition (NER)
B.Key phrase extraction
✓ C.Custom Named Entity Recognition (Custom NER)
D.Custom text classification
Why C: Custom Named Entity Recognition (Custom NER) is the correct choice because it allows you to train a model on your own labeled data (the 500 manually labeled contract clauses) to extract domain-specific entities like 'Effective Date', 'Governing Law', and 'Payment Terms' that are not covered by prebuilt models. This feature uses a custom trained model to identify and classify text spans according to your defined schema.
Last reviewed: Jun 11, 2026
Question Discussion
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