The answer is Custom Named Entity Recognition (NER). This is the correct choice because the law firm needs to extract domain-specific entities like party names, effective dates, and governing law clauses that are not available in Azure AI Language’s prebuilt entity categories. Custom NER enables you to train a model using a small set of manually annotated legal contracts, teaching the service to recognize these unique fields through a tailored extraction pipeline. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your understanding of when to use custom versus prebuilt AI Language features—a common trap is confusing Custom NER with prebuilt entity extraction or with custom text classification, which categorizes whole documents rather than pinpointing specific terms. Remember the key distinction: if the entities are unique to your domain and not covered by standard categories, Custom NER is the tool. A helpful memory tip is “Custom for the custom”—if your data has specialized labels, you need a custom model.
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 extract specific information from legal contracts, such as the names of the parties involved, effective dates, and governing law clauses. The firm has a small set of contracts that have been manually annotated with these specific fields. Which Azure AI Language feature should they use to build a custom extraction solution?
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 (NER)
Custom Named Entity Recognition (NER) is the correct choice because the law firm needs to extract specific, custom fields (party names, effective dates, governing law clauses) from legal contracts, which are not covered by prebuilt entity categories. Custom NER allows you to train a model using a small set of manually annotated contracts to recognize these domain-specific entities, enabling tailored extraction for the firm's unique 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.
✗
Prebuilt Named Entity Recognition (NER)
Why it's wrong here
Prebuilt NER recognizes common entity types but cannot be trained to extract custom fields like 'governing law clause'.
✓
Custom Named Entity Recognition (NER)
Why this is correct
Custom NER enables training on annotated examples to extract domain-specific entities, suitable for this contract scenario.
Related concept
Read the scenario before looking for a memorised answer.
✗
Key Phrase Extraction
Why it's wrong here
Key Phrase Extraction identifies general important phrases but does not extract structured entities with specific labels.
✗
Text Summarization
Why it's wrong here
Summarization produces a condensed version of the text, not extracting specific named entities.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Prebuilt NER with Custom NER, assuming that prebuilt models can be easily adapted to extract custom fields, but Azure's prebuilt NER is fixed and cannot be retrained for domain-specific entities.
Trap categories for this question
Keyword trap
Key Phrase Extraction identifies general important phrases but does not extract structured entities with specific labels.
Detailed technical explanation
How to think about this question
Custom NER in Azure AI Language uses a transformer-based model that you fine-tune on your annotated dataset, leveraging transfer learning to adapt to domain-specific entities. The service supports labeling entities in a sequential manner (e.g., using BIO tagging) and can handle overlapping or nested entities, which is critical for legal documents where clauses may contain multiple entities. In practice, a law firm might annotate 50–100 contracts to achieve high accuracy, and the model can be deployed as a REST endpoint for batch processing of new contracts.
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 (NER) — Custom Named Entity Recognition (NER) is the correct choice because the law firm needs to extract specific, custom fields (party names, effective dates, governing law clauses) from legal contracts, which are not covered by prebuilt entity categories. Custom NER allows you to train a model using a small set of manually annotated contracts to recognize these domain-specific entities, enabling tailored extraction for the firm's unique 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.
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Question Discussion
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