- A
Renaming standard NER entity types to match your organisation's terminology
Why wrong: Entity type renaming is configuration — custom NER trains a model to recognise entirely new entity types in your domain.
- B
Training a model to recognise domain-specific entities not covered by pre-built NER
Custom NER extends pre-built entity types with your own — legal clause references, medical device names, or proprietary product codes.
- C
A faster, lighter version of NER that uses simpler rules instead of machine learning
Why wrong: Rule-based NER is a traditional approach — custom NER uses machine learning trained on your labelled domain examples.
- D
Filtering NER outputs to return only the entity types relevant to your application
Why wrong: Entity type filtering is API parameter configuration — custom NER trains entirely new entity types, not just filters existing ones.
Quick Answer
The correct answer is that custom named entity recognition in Azure AI Language trains a model to identify domain-specific entities not covered by the pre-built NER model. This is correct because custom NER allows you to provide labeled examples of your own entity types—such as product codes, internal project names, or medical conditions—so the model learns to extract specialized terms unique to your organization, rather than relying on the generic categories like person or location that the pre-built model handles. On the AI-900 exam, this concept tests your understanding of when to use custom versus pre-built features, often appearing in scenario-based questions where a company needs to extract proprietary data from documents. A common trap is assuming custom NER is for improving accuracy on common entities, but it is specifically for entities the pre-built model cannot recognize. Memory tip: think “custom for company-specific, pre-built for common.”
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.
What is 'custom named entity recognition' (custom NER) in Azure AI Language?
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
Training a model to recognise domain-specific entities not covered by pre-built NER
Custom named entity recognition (custom NER) in Azure AI Language allows you to train a machine learning model to identify domain-specific entities that are not covered by the pre-built NER model. This is achieved by providing labeled examples of your own entity types, enabling the model to extract specialized terms such as product codes, internal project names, or medical conditions unique to your organization.
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.
- ✗
Renaming standard NER entity types to match your organisation's terminology
Why it's wrong here
Entity type renaming is configuration — custom NER trains a model to recognise entirely new entity types in your domain.
- ✓
Training a model to recognise domain-specific entities not covered by pre-built NER
Why this is correct
Custom NER extends pre-built entity types with your own — legal clause references, medical device names, or proprietary product codes.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
A faster, lighter version of NER that uses simpler rules instead of machine learning
Why it's wrong here
Rule-based NER is a traditional approach — custom NER uses machine learning trained on your labelled domain examples.
- ✗
Filtering NER outputs to return only the entity types relevant to your application
Why it's wrong here
Entity type filtering is API parameter configuration — custom NER trains entirely new entity types, not just filters existing ones.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing custom NER with simply renaming or filtering pre-built entity types, leading candidates to choose Option A or D, whereas custom NER requires training a model on new entity labels.
Detailed technical explanation
How to think about this question
Under the hood, custom NER uses a transformer-based model (e.g., BERT) fine-tuned on your labeled dataset via Azure AI Language's training pipeline. The model learns contextual embeddings for your custom entities, which allows it to handle ambiguous or domain-specific terms that pre-built models cannot. A real-world scenario is extracting part numbers from engineering documents, where the format (e.g., 'PN-1234-XYZ') is unique and not recognized by standard NER.
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.
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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: Training a model to recognise domain-specific entities not covered by pre-built NER — Custom named entity recognition (custom NER) in Azure AI Language allows you to train a machine learning model to identify domain-specific entities that are not covered by the pre-built NER model. This is achieved by providing labeled examples of your own entity types, enabling the model to extract specialized terms such as product codes, internal project names, or medical conditions unique to your organization.
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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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
1 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. What is the Azure AI Language feature 'custom named entity recognition' used for?
medium- A.Automatically renaming Azure resources with appropriate names
- ✓ B.Training models to recognize domain-specific entity types unique to your business
- C.Replacing personally identifiable information with pseudonyms
- D.Detecting when text contains company-specific named brands
Why B: Custom named entity recognition (NER) in Azure AI Language allows you to train a model to identify and extract domain-specific entities that are not covered by the pre-built entity catalog. This is achieved by providing labeled example data, which the service uses to learn the unique entity types relevant to your business, such as product codes, internal document IDs, or specialized medical terms.
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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