The answer is named entity recognition (NER) because it is the prebuilt Azure AI Language feature designed to extract organization names and monetary values from unstructured text without any custom training. NER works by automatically identifying and categorizing predefined entity types—such as “Organization” and “Money”—making it a perfect fit for processing thousands of legal contracts at scale. On the AI-900 exam, this question tests your understanding of which Azure AI Language prebuilt capability handles entity extraction versus other features like key phrase extraction or sentiment analysis. A common trap is confusing NER with custom text classification or entity linking, but remember that NER requires no training and outputs specific entity categories. For a quick memory tip: think “NER = Name, Entity, Recognition” and recall that it pulls out the “who” (organizations) and “how much” (monetary values) from text automatically.
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 the names of organizations and monetary values from thousands of legal contracts. They want to use a prebuilt Azure AI Language feature without custom training. Which feature should they use?
Why wrong: Key phrase extraction returns a list of important phrases but does not categorize them into types like organization or money. It is not suitable for structured entity extraction.
B
Named entity recognition
Correct. NER is designed to extract and classify entities into predefined categories, including Organization and Money, making it ideal for this task.
C
Sentiment analysis
Why wrong: Sentiment analysis determines the emotional tone (positive, negative, neutral) of text. It does not extract entities such as organizations or monetary amounts.
D
Text summarization
Why wrong: Summarization generates a concise summary of the text. It does not perform structured extraction of specific entity types.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Named entity recognition
Named entity recognition (NER) is the correct choice because it is specifically designed to identify and categorize entities such as organization names and monetary values from unstructured text. Azure AI Language's prebuilt NER model can extract these entity types without any custom training, making it ideal for processing legal contracts at scale.
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 returns a list of important phrases but does not categorize them into types like organization or money. It is not suitable for structured entity extraction.
✓
Named entity recognition
Why this is correct
Correct. NER is designed to extract and classify entities into predefined categories, including Organization and Money, making it ideal for this task.
Related concept
Read the scenario before looking for a memorised answer.
✗
Sentiment analysis
Why it's wrong here
Sentiment analysis determines the emotional tone (positive, negative, neutral) of text. It does not extract entities such as organizations or monetary amounts.
✗
Text summarization
Why it's wrong here
Summarization generates a concise summary of the text. It does not perform structured extraction of specific entity types.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse key phrase extraction with named entity recognition, assuming that extracting 'important phrases' is equivalent to identifying specific entity types like organizations and monetary values.
Trap categories for this question
Keyword trap
Key phrase extraction returns a list of important phrases but does not categorize them into types like organization or money. It is not suitable for structured entity extraction.
Detailed technical explanation
How to think about this question
Azure AI Language's NER uses a bidirectional LSTM with a CRF layer trained on a large corpus to detect entities like Organization (e.g., 'Contoso Ltd.') and Money (e.g., '$500,000'). The prebuilt model supports over 20 entity categories, including Quantity and DateTime, which can be useful for contract analysis. In practice, NER can also handle nested entities, such as 'Acme Corp's $1M settlement', extracting both the organization and the monetary value from the same phrase.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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: Named entity recognition — Named entity recognition (NER) is the correct choice because it is specifically designed to identify and categorize entities such as organization names and monetary values from unstructured text. Azure AI Language's prebuilt NER model can extract these entity types without any custom training, making it ideal for processing legal contracts at scale.
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 specific terms such as contract dates, party names, and monetary amounts from thousands of legal documents. The firm does not have a labeled dataset for custom training but needs to identify only these predefined types of information. Which prebuilt Azure AI Language feature should they use?
medium
A.Key phrase extraction
✓ B.Named Entity Recognition (NER)
C.Sentiment analysis
D.Language detection
Why B: Named Entity Recognition (NER) is the correct choice because it is a prebuilt Azure AI Language feature designed to automatically identify and categorize predefined entities such as dates, person names, and monetary amounts from text. Since the firm needs to extract specific types of information (contract dates, party names, monetary amounts) without a labeled dataset, NER's out-of-the-box models can directly recognize these common entity categories without any custom training.
Last reviewed: Jun 11, 2026
Question Discussion
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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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