The answer is Named Entity Recognition (NER). This built-in Azure AI Language feature is specifically designed to automatically identify and categorize mentions of people, organizations, and locations within unstructured text, using pre-trained models that require no labeled training data. Because the news agency lacks custom datasets, NER works out-of-the-box to tag entities directly, improving search and categorization without any manual annotation or custom training. On the AI-900 exam, this scenario tests your understanding of pre-configured Azure Cognitive Services versus custom solutions like Custom Text Classification; a common trap is confusing NER with entity linking or key phrase extraction, but NER is the only one focused purely on identifying named entities like people, places, and companies. For a quick memory tip, remember that NER “names the entities” in your text—just like a news reporter tagging who, where, and what in a story.
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 news agency receives thousands of articles daily from wire services. They want to automatically identify and tag mentions of people, organizations, and locations within each article to improve search and categorization. The agency has no labeled training data. Which built-in Azure AI Language feature should they use?
Why wrong: Sentiment Analysis determines the overall emotional tone of the text, such as positive, negative, or neutral. It does not extract named entities.
B
Key Phrase Extraction
Why wrong: Key Phrase Extraction returns a list of important words or phrases from the text but does not classify them into categories like person, organization, or location.
C
Named Entity Recognition (NER)
NER automatically identifies and categorizes named entities such as persons, organizations, and locations. It requires no training data and is directly suitable for this task.
D
Language Detection
Why wrong: Language Detection identifies the natural language in which the text is written (e.g., English, French). It does not extract entities.
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 choice because it is specifically designed to identify and categorize mentions of people, organizations, locations, and other entity types in unstructured text. Since the agency has no labeled training data, NER's pre-trained model can be used out-of-the-box without any custom training, making it ideal for automatically tagging articles with these entity types to improve search and categorization.
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 overall emotional tone of the text, such as positive, negative, or neutral. It does not extract named entities.
✗
Key Phrase Extraction
Why it's wrong here
Key Phrase Extraction returns a list of important words or phrases from the text but does not classify them into categories like person, organization, or location.
✓
Named Entity Recognition (NER)
Why this is correct
NER automatically identifies and categorizes named entities such as persons, organizations, and locations. It requires no training data and is directly suitable for this task.
Related concept
Read the scenario before looking for a memorised answer.
✗
Language Detection
Why it's wrong here
Language Detection identifies the natural language in which the text is written (e.g., English, French). It does not extract entities.
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, thinking that extracting important phrases is the same as identifying specific entity types, but Key Phrase Extraction does not categorize phrases into predefined classes like person or organization.
Trap categories for this question
Keyword trap
Key Phrase Extraction returns a list of important words or phrases from the text but does not classify them into categories like person, organization, or location.
Detailed technical explanation
How to think about this question
Azure AI Language's NER uses a transformer-based model trained on a large corpus to recognize entities across multiple categories, including Person, Organization, Location, DateTime, and more. Under the hood, it employs a bidirectional encoder that captures context from both directions, enabling it to disambiguate entities like 'Washington' (location vs. person) based on surrounding words. In a real-world scenario, NER can also handle multi-word entities (e.g., 'New York City') and return confidence scores for each detected entity, which the agency could use to filter low-confidence tags.
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 choice because it is specifically designed to identify and categorize mentions of people, organizations, locations, and other entity types in unstructured text. Since the agency has no labeled training data, NER's pre-trained model can be used out-of-the-box without any custom training, making it ideal for automatically tagging articles with these entity types to improve search and categorization.
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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