Overview
Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that enables machines to understand, interpret, and generate human language. For the AI-900: Microsoft Azure AI Fundamentals exam, the goal is not to build language models, but to recognize NLP workloads, understand what problems they solve, and identify when NLP is the correct AI approach.
This topic appears under:
- Describe Artificial Intelligence workloads and considerations (15–20%)
- Identify features of common AI workloads
Most exam questions will be scenario-based, asking you to choose the correct AI workload based on how text is used.
What Is a Natural Language Processing Workload?
A natural language processing workload involves analyzing or generating language in written or spoken form (after speech has been converted to text).
NLP workloads typically:
- Process unstructured text
- Extract meaning, sentiment, or intent
- Translate between languages
- Generate human-like text responses
Common inputs:
- Emails, chat messages, documents
- Social media posts
- Customer reviews
- Transcribed speech
Common outputs:
- Sentiment scores
- Extracted keywords or entities
- Translated text
- Generated responses or summaries
Common Natural Language Processing Use Cases
On the AI-900 exam, NLP workloads are presented through everyday business scenarios. The following are the most important ones to recognize.
Text Classification
What it does: Categorizes text into predefined labels.
Example scenarios:
- Classifying emails as spam or not spam
- Routing support tickets by topic
- Detecting abusive or inappropriate content
Key idea: The system assigns one or more labels to a piece of text.
Sentiment Analysis
What it does: Determines the emotional tone of text.
Example scenarios:
- Analyzing customer reviews to see if feedback is positive or negative
- Monitoring social media reactions to a product launch
Key idea: Sentiment analysis focuses on opinion and emotion, not topic.
Key Phrase Extraction
What it does: Identifies the main concepts discussed in a document.
Example scenarios:
- Summarizing customer feedback
- Highlighting important terms in legal or technical documents
Key idea: Key phrases help quickly understand what a document is about.
Named Entity Recognition (NER)
What it does: Identifies and categorizes entities in text.
Common entity types:
- People
- Organizations
- Locations
- Dates and numbers
Example scenarios:
- Extracting company names from contracts
- Identifying people and places in news articles
Language Detection
What it does: Identifies the language used in a text sample.
Example scenarios:
- Detecting the language of customer messages before translation
- Routing requests to region-specific support teams
Language Translation
What it does: Converts text from one language to another.
Example scenarios:
- Translating product descriptions for global audiences
- Providing multilingual customer support
Key idea: This workload focuses on preserving meaning, not word-for-word translation.
Question Answering and Conversational AI
What it does: Understands user questions and generates relevant responses.
Example scenarios:
- Customer support chatbots
- FAQ systems
- Virtual assistants
Key idea: The system interprets intent and responds in natural language.
Text Summarization
What it does: Condenses long documents into shorter summaries.
Example scenarios:
- Summarizing reports or meeting notes
- Highlighting key points from articles
Azure Services Commonly Associated with NLP
For AI-900, you should recognize these services at a conceptual level.
Azure AI Language
Supports:
- Sentiment analysis
- Text classification
- Key phrase extraction
- Named entity recognition
- Language detection
- Summarization
This is the primary service referenced for NLP workloads on the exam.
Azure AI Translator
Supports:
- Text translation between languages
Used specifically when scenarios mention multilingual translation.
Azure AI Bot Service
Supports:
- Conversational AI solutions
Often appears alongside NLP services when building chatbots.
How NLP Differs from Other AI Workloads
Distinguishing NLP from other workloads is a common exam requirement.
| AI Workload Type | Primary Input |
|---|---|
| Natural Language Processing | Text |
| Speech AI | Audio |
| Computer Vision | Images and video |
| Anomaly Detection | Numerical or time-series data |
Exam tip: If the data is text-based and the goal is to understand meaning, sentiment, or intent, it is an NLP workload.
Responsible AI Considerations
NLP systems can introduce risks if not used responsibly.
Key considerations include:
- Bias in language models
- Offensive or harmful content generation
- Data privacy when analyzing personal communications
AI-900 tests awareness, not mitigation techniques.
Exam Tips for Identifying NLP Workloads
- Look for keywords like text, email, message, document, review, chat
- Identify the goal: classify, analyze sentiment, extract meaning, translate, or respond
- Ignore implementation details—focus on what problem is being solved
- Choose the simplest AI workload that meets the scenario
Summary
For the AI-900 exam, you should be able to:
- Recognize when a scenario represents a natural language processing workload
- Identify common NLP use cases and capabilities
- Associate NLP scenarios with Azure AI Language and related services
- Distinguish NLP from speech, vision, and other AI workloads
A solid understanding of NLP workloads will significantly improve your confidence across multiple exam questions.
Go to the Practice Exam Questions for this topic.
Go to the PL-300 Exam Prep Hub main page.
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