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 needs to automatically process thousands of news articles and extract specific structured information: the publication date, the author's name, and the headline. None of these fields are standard predefined entities in Azure AI Language. Which Azure AI Language feature should they use to train a model to extract these custom entities?
⚠ Common exam trap
Many exam-takers confuse custom NER with key phrase extraction, assuming key phrases can extract structured fields like dates or names, but key phrases are untyped and not designed for entity-level extraction.
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
✓
Custom Named Entity Recognition (Custom NER)
Custom Named Entity Recognition (Custom NER) is the correct choice because it allows you to train a model to extract domain-specific entities—such as publication date, author name, and headline—that are not part of Azure's predefined entity catalog. By providing labeled examples, Custom NER learns to identify these custom fields from unstructured text, enabling automated extraction from thousands of news articles.
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 the most salient words or phrases in a document without assigning them a semantic category or linking them to a schema. It is a linguistics-based feature that surfaces topics like "machine learning" or "stock market" but does not produce typed fields such as a byline or a publication date. For extracting structured metadata like author names and specific dates, you need a model capable of entity recognition and classification, which key phrase extraction intentionally does not provide.
- ✗
Sentiment analysis
Why it's wrong here
Sentiment analysis evaluates the emotional polarity of text, assigning scores for positive, negative, or neutral tone at document and sentence levels. It is a subjective classification task that has no mechanism to identify or extract specific data points such as people, organizations, dates, or headline text. Because it focuses on the writer's attitude rather than factual content, it cannot fill a structured record for an article, making it unsuitable for the agency's automated ingestion pipeline.
- ✓
Custom Named Entity Recognition (Custom NER)
Why this is correct
Custom Named Entity Recognition (Custom NER) is the correct choice because it lets you train a model using labeled examples to recognize your own entity types, such as author bylines, publication dates, and headline phrases that the prebuilt entity set may not cover. The service uses the same underlying text analytics engine but starts from a custom schema you define, then learns to tag those entities in thousands of new articles. By deploying the trained model through Azure AI Language, the news agency can automatically transform unstructured article text into structured records with the exact fields they need.
- ✗
Language detection
Why it's wrong here
Language detection identifies the primary language of a document by analyzing its script and character distribution, returning a language code and a confidence score. It is a coarse-grained classification task that does not parse or extract any specific information from the content, so it cannot recognize an author, a date, or the headline. This feature answers only 'what language is this?' and therefore plays no direct role in capturing the structured metadata required for the news archive.
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Azure Machine Learning Studio
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
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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