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 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?
Why wrong: Sentiment analysis determines the emotional tone (positive, negative, neutral) of text, not specific named entities.
B
Key phrase extraction
Why wrong: Key phrase extraction returns the most important phrases in text but does not specifically identify entities like names, dates, or amounts in a structured way.
C
Named Entity Recognition (NER)
NER identifies and categorizes entities into predefined types such as Person, Date, Quantity, and Money, which directly meets the firm's requirement.
D
Language detection
Why wrong: Language detection identifies the language in which the text is written, but does not extract entities from the content.
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 (NER)
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.
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.
✗
Sentiment analysis
Why it's wrong here
Sentiment analysis determines the emotional tone (positive, negative, neutral) of text, not specific named entities.
✗
Key phrase extraction
Why it's wrong here
Key phrase extraction returns the most important phrases in text but does not specifically identify entities like names, dates, or amounts in a structured way.
✓
Named Entity Recognition (NER)
Why this is correct
NER identifies and categorizes entities into predefined types such as Person, Date, Quantity, and Money, which directly meets the firm's requirement.
Related concept
Read the scenario before looking for a memorised answer.
✗
Language detection
Why it's wrong here
Language detection identifies the language in which the text is written, but does not extract entities from the content.
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, but key phrase extraction does not categorize phrases into specific entity types like dates or monetary amounts, which is the core requirement in this question.
Trap categories for this question
Keyword trap
Key phrase extraction returns the most important phrases in text but does not specifically identify entities like names, dates, or amounts in a structured way.
Detailed technical explanation
How to think about this question
Under the hood, Azure NER uses pre-trained machine learning models that tokenize text and apply sequence labeling (e.g., BIO tagging) to classify each token into entity categories like Person, Date, or Money. A subtle behavior is that NER can handle multi-word entities (e.g., 'John Doe' as a single Person entity) and can be customized with custom NER models for domain-specific terms. In a real-world scenario, a legal firm might use NER to automatically populate a database with contract metadata, reducing manual review time.
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: Named Entity Recognition (NER) — 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.
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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