AI, AI-900, Artificial Intelligence (AI), Microsoft Certification, Natural Language Processing (NLP)

Describe Capabilities of the Azure AI Language Service (AI-900 Exam Prep)

Where This Fits in the Exam

  • Exam: AI-900 – Microsoft Azure AI Fundamentals
  • Domain: Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)
  • Sub-area: Identify Azure tools and services for NLP workloads

At this level, the exam focuses on what the service does, when to use it, and how it differs from other Azure AI services—not on implementation or coding.

What Is the Azure AI Language Service?

The Azure AI Language service is a cloud-based NLP service that enables applications to understand, analyze, and extract meaning from text.

It brings together several NLP capabilities under a single unified service, making it easier to build text-based AI solutions such as:

  • Customer feedback analysis
  • Chatbots
  • Document processing
  • Knowledge mining

For AI-900, think of it as “the main Azure service for understanding text.”

Key Capabilities of the Azure AI Language Service

1. Text Analytics

Text Analytics allows applications to analyze raw text and extract insights.

Main features include:

  • Sentiment analysis
  • Key phrase extraction
  • Named entity recognition
  • Language detection

These features are widely tested on the exam.

2. Sentiment Analysis

What it does:
Determines whether text expresses a positive, negative, neutral, or mixed sentiment.

Example use cases:

  • Analyzing customer reviews
  • Measuring brand perception on social media
  • Evaluating survey responses

Exam tip:
Sentiment analysis answers “How does the text feel?”

3. Key Phrase Extraction

What it does:
Identifies the main talking points in a block of text.

Example:

“The hotel had great service but poor Wi-Fi.”

Key phrases might include:

  • great service
  • poor Wi-Fi

Common exam scenario:
Summarizing long documents or feedback automatically.

4. Named Entity Recognition (NER)

What it does:
Detects and categorizes entities mentioned in text.

Common entity types:

  • People
  • Organizations
  • Locations
  • Dates
  • Products

Example:

“Satya Nadella is the CEO of Microsoft.”

Entities detected:

  • Person: Satya Nadella
  • Organization: Microsoft

5. Language Detection

What it does:
Identifies the language a piece of text is written in.

Why it matters:

  • Enables multilingual applications
  • Often used before translation or sentiment analysis

Exam tip:
Azure AI Language can detect language without being told what it is.

6. Question Answering

What it does:
Allows applications to answer natural language questions based on provided content.

Key points:

  • Replaces the older QnA Maker
  • Uses FAQs, documents, or URLs as knowledge sources
  • Commonly used in chatbots and helpdesk systems

Example:

User: “What is your return policy?”
Bot responds using stored knowledge.

7. Text Classification

What it does:
Assigns predefined categories or labels to text.

Examples:

  • Classifying emails as billing, technical support, or general inquiry
  • Tagging support tickets automatically

Important distinction:
This is about categorizing content, not detecting sentiment.

8. Custom Language Models

What it does:
Allows organizations to train custom NLP models using their own data.

Used for:

  • Domain-specific terminology
  • Industry-specific language (legal, healthcare, finance)

AI-900 focus:
Know that customization is possible, not how to train models.

Azure AI Language Service vs Other Azure AI Services

This distinction is frequently tested.

ServicePrimary Purpose
Azure AI LanguageUnderstand and analyze text
Azure TranslatorTranslate text between languages
Azure SpeechSpeech-to-text and text-to-speech
Azure VisionAnalyze images and video

Exam shortcut:
If the scenario is about meaning, sentiment, or structure of text, the answer is usually Azure AI Language service.

Common Exam Scenarios to Watch For

You’ll often see questions like:

  • “Which Azure service should you use to analyze customer reviews?”
  • “Which service extracts people and locations from documents?”
  • “Which NLP service powers chatbots with question answering?”

If it involves text understanding, not translation or speech → Azure AI Language.

Key Takeaways for AI-900

  • Azure AI Language service is the primary NLP service for text analysis
  • It supports:
    • Sentiment analysis
    • Key phrase extraction
    • Entity recognition
    • Language detection
    • Question answering
    • Text classification
  • It is different from Translator and Speech
  • AI-900 focuses on capabilities and use cases, not APIs or code

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