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 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?
⚠ Common exam trap
Many candidates 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.
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 (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.
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 Azure AI Language to assign an overall positive, negative, neutral, or mixed score to each article based on affective language and opinion. Because wire article metadata requires identifying actual referenced entities such as a politician's name or a company, sentiment polarity alone cannot produce that structured output. Therefore it is unsuitable for extracting named entities.
- ✗
Key Phrase Extraction
Why it's wrong here
Key Phrase Extraction outputs the most relevant words or phrases from a document, such as 'climate summit' or 'tech company', but returns them as unlabeled strings. It does not assign semantic types like Person, Organization, or Location, and it does not resolve which phrase is an entity. NER is required to infer that a token is, say, a named person rather than merely a topic.
- ✓
Named Entity Recognition (NER)
Why this is correct
Named Entity Recognition (NER) in Azure AI Language is the correct choice because it automatically scans each article and identifies mentions of people, organizations, locations, dates, and quantities, labeling each with a predefined category. It is a pretrained capability, so the news agency can use it immediately without creating custom training data. The resulting structured tags enable automated indexing and alerting across thousands of daily wire stories.
- ✗
Language Detection
Why it's wrong here
Language Detection identifies which natural language a wire article is written in, returning the language name, ISO code, and a confidence score for routing or translation workflows. This operation does not inspect semantic content to find names of people or places. Consequently, it cannot provide the entity-level metadata the news agency needs.
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Types of AI Workloads
Key term
Training data
Training data is a set of examples used to teach a machine learning model how to make predictions or decisions.
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
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