Describe features of Natural Language Processing workloads on Azure →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
A news agency wants to automatically extract the names of people and organizations mentioned in thousands of articles. They plan to use a prebuilt Azure AI Language feature that requires no custom training. Which feature should they use?
⚠ Common exam trap
Many candidates confuse key phrase extraction with named entity recognition, but key phrase extraction returns multi-word phrases without entity type classification, while NER specifically labels entities like 'Person' or 'Organization'.
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
✓
Named entity recognition
Named entity recognition (NER) is the correct feature because it is specifically designed to identify and categorize entities such as people, organizations, locations, and dates from unstructured text. The news agency's requirement to extract names of people and organizations from articles matches NER's prebuilt capability, which requires no custom training and is available in Azure AI Language.
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 uses NLP to identify the emotional tone or polarity (positive, negative, neutral) expressed in a document or sentence. It operates over the entire text or segment, producing an aggregate opinion score, not a list of mentioned entities. Because it lacks any mechanism to locate and classify individual tokens as person names, it cannot serve the agency's need to extract names.
When this WOULD be correct
A question asking which Azure AI Language feature to use for determining public opinion or emotional tone in customer reviews or social media posts would make sentiment analysis correct.
- ✓
Named entity recognition
Why this is correct
Named entity recognition (NER) is a sequence-labeling task that scans unstructured text and marks tokens or spans as belonging to predefined entity types, such as PER (person), ORG (organization), or GPE (geopolitical entity). Modern NER models, often transformer-based, produce a tagged output (e.g., 'John' as PERSON) for each mention. This directly yields the names of people mentioned, making it the correct choice for the agency.
- ✗
Key phrase extraction
Why it's wrong here
Key phrase extraction returns a ranked set of single words or multi-word expressions that best represent the main topics of a document, typically using statistical measures like TF-IDF or graph-based ranking like TextRank. It does not apply a semantic type to any extracted phrase, so a detected person's name would be indistinguishable from any other phrase. The output is a list of 'topics', not structured entities, so it fails to provide a reliable list of people's names.
When this WOULD be correct
A question asking for a feature to automatically extract the main topics or important terms from documents without needing to identify specific named entities (e.g., 'Which feature extracts the most relevant words or phrases from text?').
- ✗
Language detection
Why it's wrong here
Language detection classifies the natural language (e.g., en, fr, de) of a given text based on character frequency, n-grams, or Unicode script features. It outputs a binary or multi-class language label for the whole document or line, and it never inspects individual words for semantic meaning. Consequently, it cannot recognize that a token like 'Sofia' is a person's name, so it is entirely orthogonal to entity extraction.
When this WOULD be correct
A question asking which Azure AI Language feature to use for identifying the language of customer reviews or support tickets before routing them to appropriate language-specific processing pipelines.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓Named entity recognitionCorrect answer▾
Why this is correct
Named entity recognition (NER) is a sequence-labeling task that scans unstructured text and marks tokens or spans as belonging to predefined entity types, such as PER (person), ORG (organization), or GPE (geopolitical entity). Modern NER models, often transformer-based, produce a tagged output (e.g., 'John' as PERSON) for each mention. This directly yields the names of people mentioned, making it the correct choice for the agency.
✗Sentiment analysisWrong answer — click to see why▾
Why this is wrong here
Sentiment analysis determines the emotional tone (positive, negative, neutral) of text, not the extraction of named entities like people and organizations.
★ When this WOULD be the correct answer
A question asking which Azure AI Language feature to use for determining public opinion or emotional tone in customer reviews or social media posts would make sentiment analysis correct.
Why candidates choose this
Candidates may confuse 'extracting names' with 'analyzing content' and think sentiment analysis is a general-purpose text analysis tool that can identify entities.
✗Key phrase extractionWrong answer — click to see why▾
Why this is wrong here
Key phrase extraction identifies important terms and concepts but does not specifically extract named entities like people and organizations, which is the requirement here.
★ When this WOULD be the correct answer
A question asking for a feature to automatically extract the main topics or important terms from documents without needing to identify specific named entities (e.g., 'Which feature extracts the most relevant words or phrases from text?').
Why candidates choose this
Candidates may confuse key phrase extraction with named entity recognition because both involve extracting important information from text, but key phrases are not limited to named entities.
✗Language detectionWrong answer — click to see why▾
Why this is wrong here
Language detection identifies the language of text, not entities like people or organizations, so it cannot extract names from articles.
★ When this WOULD be the correct answer
A question asking which Azure AI Language feature to use for identifying the language of customer reviews or support tickets before routing them to appropriate language-specific processing pipelines.
Why candidates choose this
Candidates may confuse language detection with entity extraction, thinking that detecting the language is a prerequisite for extracting names, or they may misread the question as asking about language-related features.
Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Go deeper
Related to this question
Learn chapter
Azure Machine Learning Studio
Key term
Feature
A feature is a distinct unit of functionality that delivers value to the user, often managed and tracked throughout the software development lifecycle.
Key term
Azure AI Language
Azure AI Language is a cloud-based service from Microsoft that uses natural language processing to understand, analyze, and generate human language for applications.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.