Tag: Azure AI Language

AI, AI-900, Artificial Intelligence (AI), Microsoft Certification, Natural Language Processing (NLP) January 31, 2026

Identify Features and Uses for Sentiment Analysis (AI-900 Exam Prep)

Overview

Sentiment analysis is a Natural Language Processing (NLP) capability that determines the emotional tone or opinion expressed in text. In the context of the AI-900 exam, sentiment analysis is tested as a foundational NLP workload and is typically associated with scenarios involving customer feedback, reviews, social media posts, and support interactions.

On Azure, sentiment analysis is provided through Azure AI Language, which offers pretrained models that can analyze text without requiring machine learning expertise.

What Is Sentiment Analysis?

Sentiment analysis evaluates text to identify:

Overall sentiment (positive, negative, neutral, or mixed)
Confidence scores indicating how strongly the sentiment is expressed
Sentence-level sentiment (in addition to document-level sentiment)
Opinion mining (identifying sentiment about specific aspects, at a high level)

Example:

“The product works great, but the delivery was slow.”

Sentiment analysis can identify:

Positive sentiment about the product
Negative sentiment about the delivery
An overall mixed sentiment for the entire text

Azure Service Used for Sentiment Analysis

Sentiment analysis is a feature of:

Azure AI Language

Part of Azure AI Services, Azure AI Language provides several NLP capabilities, including:

Sentiment analysis
Key phrase extraction
Entity recognition
Language detection

For AI-900:

No custom model training is required
Prebuilt models are used
Text can be analyzed via REST APIs or SDKs

Key Features of Sentiment Analysis

1. Sentiment Classification

Text is classified into:

Positive
Negative
Neutral
Mixed

This classification applies at both:

Document level
Sentence level

2. Confidence Scores

Each sentiment classification includes a confidence score, indicating how strongly the model believes the sentiment applies.

Example:

Positive: 0.92
Neutral: 0.05
Negative: 0.03

Higher confidence scores indicate stronger sentiment.

3. Multi-Language Support

Azure AI Language supports sentiment analysis across multiple languages, making it suitable for global applications.

4. Pretrained Models

Sentiment analysis:

Uses pretrained AI models
Requires no labeled data
Can be implemented quickly

This aligns with the AI-900 focus on using AI services rather than building models.

Common Use Cases for Sentiment Analysis

1. Customer Feedback Analysis

Analyze:

Product reviews
Surveys
Net Promoter Score (NPS) comments

Goal: Understand customer satisfaction trends at scale.

2. Social Media Monitoring

Organizations analyze social media posts to:

Track brand perception
Identify emerging issues
Measure reaction to announcements or campaigns

3. Support Ticket Prioritization

Sentiment analysis can help:

Identify frustrated or angry customers
Escalate negative interactions automatically
Improve response times

4. Market Research

Sentiment analysis helps companies understand:

Public opinion about competitors
Trends in consumer sentiment
Product reception after launch

What Sentiment Analysis Is NOT Used For

This distinction is commonly tested on the exam.

Task	Correct Capability
Extract names or dates	Entity recognition
Identify important topics	Key phrase extraction
Translate text	Translation
Detect emotional tone	Sentiment analysis

Sentiment Analysis vs Related NLP Features

Sentiment Analysis vs Key Phrase Extraction

Sentiment analysis: How does the user feel?
Key phrase extraction: What is the text about?

Sentiment Analysis vs Entity Recognition

Sentiment analysis: Emotional tone
Entity recognition: Specific items (people, places, dates)

AI-900 Exam Tips 💡

Focus on when to use sentiment analysis, not how to implement it
Expect scenario-based questions (customer reviews, feedback, tweets)
Remember: Sentiment analysis is part of Azure AI Language
No training, tuning, or ML pipelines are required for AI-900

Summary

Sentiment analysis is a core NLP workload that enables organizations to automatically evaluate opinions and emotions in text. For the AI-900 exam, you should understand:

What sentiment analysis does
Common real-world use cases
How it differs from other NLP features
That it is delivered through Azure AI Language using pretrained models

Go to the Practice Exam Questions for this topic.

Go to the AI-900 Exam Prep Hub main page.

AI, AI-900, Artificial Intelligence (AI), Microsoft Certification, Natural Language Processing (NLP) January 31, 2026

Practice Questions: Identify Features and Uses for Language Modeling (AI-900 Exam Prep)

Practice Questions

Question 1

What is the primary purpose of a language model in natural language processing?

A. To detect objects in images
B. To classify numerical data
C. To predict and generate sequences of text
D. To translate speech into audio

Correct Answer: C

Explanation:
Language models are designed to predict and generate text based on learned language patterns. They analyze sequences of words to understand context and produce meaningful text.

Question 2

Which scenario is the best example of a language modeling workload?

A. Detecting faces in an image
B. Generating a human-like response in a chatbot
C. Identifying key phrases in a document
D. Extracting entities such as names and locations

Correct Answer: B

Explanation:
Chatbots rely on language models to understand user input and generate natural language responses, which is a core language modeling capability.

Question 3

A company wants an AI system that can automatically complete sentences as users type. Which NLP capability is required?

A. Sentiment analysis
B. Entity recognition
C. Language modeling
D. Optical character recognition

Correct Answer: C

Explanation:
Sentence and text completion depend on predicting the next word or phrase, which is a fundamental feature of language modeling.

Question 4

Which feature distinguishes modern language models from earlier rule-based NLP systems?

A. They rely only on predefined grammar rules
B. They can understand context across multiple words or sentences
C. They only work with structured data
D. They require manual labeling of every sentence

Correct Answer: B

Explanation:
Modern language models use context to generate coherent responses, allowing them to understand meaning beyond individual words.

Question 5

Which Azure service provides access to advanced pretrained language models for text generation and conversational AI?

A. Azure AI Vision
B. Azure AI Language
C. Azure OpenAI Service
D. Azure Form Recognizer

Correct Answer: C

Explanation:
The Azure OpenAI Service provides access to large pretrained language models that support text generation, chat, summarization, and reasoning.

Question 6

A customer support system automatically answers user questions using natural language. Which AI capability is primarily being used?

A. Object detection
B. Language modeling
C. Key phrase extraction
D. Speech synthesis

Correct Answer: B

Explanation:
Automatically generating answers in natural language relies on language modeling, especially for conversational and question-answering scenarios.

Question 7

Which task is least likely to use language modeling?

A. Generating a summary of a document
B. Translating text between languages
C. Detecting the sentiment of a sentence
D. Producing a chatbot response

Correct Answer: C

Explanation:
Sentiment analysis focuses on identifying emotional tone, not generating or predicting text. The other options rely heavily on language modeling.

Question 8

Why are pretrained language models commonly used in Azure AI solutions?

A. They eliminate the need for any data
B. They require less storage than traditional models
C. They can be used immediately without custom training
D. They only support English language text

Correct Answer: C

Explanation:
Pretrained models are already trained on large datasets and can be used out of the box, which aligns with Azure’s AI service approach.

Question 9

Which statement best describes how language models generate text?

A. By randomly selecting words from a dictionary
B. By applying fixed grammatical rules
C. By predicting the most likely next word in a sequence
D. By translating text into numerical values only

Correct Answer: C

Explanation:
Language models generate text by calculating probabilities of word sequences and selecting the most likely continuation.

Question 10

A solution needs to create readable paragraphs based on a short prompt provided by a user. Which AI capability should be used?

A. Optical character recognition
B. Speech recognition
C. Language modeling
D. Image classification

Correct Answer: C

Explanation:
Generating paragraphs from a prompt is a classic language modeling use case involving text prediction and generation.

Quick Exam Tip

If the question involves:

Text generation
Chatbots
Predicting or completing text
Understanding context in language

👉 It’s a good chance it involves Language Modeling

Go to the AI-900 Exam Prep Hub main page.

AI, AI-900, Artificial Intelligence (AI), Microsoft Certification, Natural Language Processing (NLP) January 31, 2026

Identify Features and Uses for Speech Recognition and Synthesis (AI-900 Exam Prep)

Where This Fits in the Exam

Exam area: Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)
Sub-area: Identify features of common NLP workload scenarios
Key focus: Understanding what speech recognition and synthesis do, when to use them, and which Azure services support them

This topic is highly scenario-driven on the exam.

Overview: Speech in NLP Workloads

Speech-related NLP workloads allow AI systems to:

Understand spoken language (speech recognition)
Generate spoken language (speech synthesis)

Together, these capabilities enable voice-based interactions such as virtual assistants, voice bots, dictation tools, and accessibility solutions.

Speech Recognition

What Is Speech Recognition?

Speech recognition (also called speech-to-text) is the process of converting spoken audio into written text.

The AI system analyzes:

Audio signals
Phonemes and pronunciation
Language patterns
Context

And produces text that represents what was spoken.

Key Features of Speech Recognition

Speech recognition solutions can:

Convert live or recorded audio into text
Support real-time transcription
Handle multiple languages and accents
Apply noise reduction
Recognize custom vocabulary (e.g., medical or technical terms)
Provide timestamps for spoken words or phrases

Common Uses of Speech Recognition

Speech recognition is used when users speak instead of type.

Common scenarios include:

Voice commands (e.g., “Turn on the lights”)
Call center transcription
Meeting and lecture transcription
Voice-controlled applications
Accessibility tools for users with limited mobility
Voice input for chatbots and virtual assistants

Azure Services for Speech Recognition

In Azure, speech recognition is provided by:

Azure AI Speech (Speech service)

Capabilities include:

Speech-to-text
Real-time and batch transcription
Language detection
Custom speech models

Speech Synthesis

What Is Speech Synthesis?

Speech synthesis (also called text-to-speech) is the process of converting written text into spoken audio.

The goal is to produce natural, human-like speech that sounds fluent and expressive.

Key Features of Speech Synthesis

Speech synthesis solutions can:

Convert text into spoken audio
Use natural-sounding neural voices
Support multiple languages and accents
Adjust:
- Pitch
- Speed
- Tone
Apply SSML (Speech Synthesis Markup Language) for fine control
Generate speech for audio files or real-time playback

Common Uses of Speech Synthesis

Speech synthesis is used when systems need to speak to users.

Common scenarios include:

Virtual assistants and chatbots
Navigation and GPS systems
Accessibility tools for visually impaired users
Audiobooks and e-learning content
Automated announcements
Customer service voice bots

Azure Services for Speech Synthesis

In Azure, speech synthesis is also provided by:

Azure AI Speech (Speech service)

Capabilities include:

Text-to-speech
Neural voices
Voice customization
Multilingual speech output

Speech Recognition vs Speech Synthesis

Capability	Speech Recognition	Speech Synthesis
Direction	Speech → Text	Text → Speech
Input	Audio	Text
Output	Text	Audio
Common Name	Speech-to-text	Text-to-speech
Example	Transcribing a call	Reading text aloud

Combined Speech Workloads

Many real-world solutions use both capabilities together.

Example:

User speaks a question (speech recognition)
System processes the text using NLP or AI logic
System responds verbally (speech synthesis)

This is the foundation of:

Voice assistants
Conversational AI
Interactive voice response (IVR) systems

Exam-Focused Clues to Watch For 👀

On the AI-900 exam, speech workloads are usually described using phrases like:

“Convert spoken audio into text” → Speech recognition
“Generate spoken responses from text” → Speech synthesis
“Voice-enabled application” → Azure AI Speech
“Real-time transcription” → Speech recognition
“Reads text aloud” → Speech synthesis

Key Takeaways for AI-900

Speech recognition converts speech to text
Speech synthesis converts text to speech
Both are part of NLP workloads
Azure AI Speech is the primary Azure service for both
Common exam scenarios involve:
- Voice assistants
- Transcription
- Accessibility
- Customer service automation

Go to the Practice Exam Questions for this topic.

Go to the AI-900 Exam Prep Hub main page.

AI, AI-900, Artificial Intelligence (AI), Microsoft Certification, Natural Language Processing (NLP) January 31, 2026

Practice Questions: Identify Features and Uses for Translation (AI-900 Exam Prep)

Practice Questions

Question 1

Which Azure service is primarily used to translate text between languages?

A. Azure Speech Service
B. Azure Language Service
C. Azure Translator
D. Azure OpenAI Service

✅ Correct Answer: C. Azure Translator

Explanation:
Azure Translator (part of Azure AI Services) is specifically designed for text translation across multiple languages. While other services handle NLP or speech, Translator focuses on multilingual text conversion.

Question 2

A company wants to translate product descriptions on a website in real time for international users. Which feature of Azure Translator best supports this scenario?

A. Batch transcription
B. Real-time REST API translation
C. Sentiment analysis
D. Custom question answering

✅ Correct Answer: B. Real-time REST API translation

Explanation:
Azure Translator provides REST APIs that allow applications and websites to translate text dynamically as users access content.

Question 3

Which scenario is the best example of using machine translation?

A. Detecting the emotional tone of customer feedback
B. Extracting key phrases from documents
C. Translating an email from English to French
D. Identifying people and locations in text

✅ Correct Answer: C. Translating an email from English to French

Explanation:
Machine translation focuses on converting text from one language to another, which is exactly what this scenario describes.

Question 4

What type of translation does Azure Translator perform by default?

A. Rule-based translation
B. Human-assisted translation
C. Statistical translation
D. Neural machine translation

✅ Correct Answer: D. Neural machine translation

Explanation:
Azure Translator uses Neural Machine Translation (NMT) models, which rely on deep learning to produce more natural and accurate translations.

Question 5

A travel application needs to detect the source language of user input before translating it. Can Azure Translator support this requirement?

A. No, language detection requires Azure Language Service
B. Yes, language detection is built into Azure Translator
C. Only if custom models are trained
D. Only for speech input

✅ Correct Answer: B. Yes, language detection is built into Azure Translator

Explanation:
Azure Translator can automatically detect the source language of text before translating it, which is a common real-world scenario.

Question 6

Which of the following is a common use case for translation in Azure?

A. Voice-controlled virtual assistants
B. Multilingual customer support chatbots
C. Facial recognition systems
D. Predictive maintenance systems

✅ Correct Answer: B. Multilingual customer support chatbots

Explanation:
Translation enables chatbots and support systems to communicate with users in multiple languages, improving global accessibility.

Question 7

A company needs consistent translation for industry-specific terminology (for example, legal or medical terms). What Azure Translator feature helps with this?

A. Language detection
B. Speech synthesis
C. Custom Translator
D. Sentiment scoring

✅ Correct Answer: C. Custom Translator

Explanation:
Custom Translator allows organizations to train translation models using their own terminology, improving accuracy for specialized domains.

Question 8

Which input format is supported by Azure Translator?

A. Text only
B. Audio only
C. Text and images
D. Text only (speech requires another service)

✅ Correct Answer: D. Text only (speech requires another service)

Explanation:
Azure Translator works with text input. For speech-to-speech translation, Azure Speech Service is used in combination with translation.

Question 9

Which Azure service would you combine with Azure Translator to build a speech-to-speech translation application?

A. Azure Vision Service
B. Azure Speech Service
C. Azure Language Service
D. Azure Bot Service only

✅ Correct Answer: B. Azure Speech Service

Explanation:
Speech-to-speech translation requires speech recognition (speech-to-text) and speech synthesis (text-to-speech), which are handled by Azure Speech Service, alongside translation.

Question 10

Why is translation considered a core Natural Language Processing (NLP) workload?

A. It analyzes numerical data patterns
B. It processes and understands human language
C. It detects objects in images
D. It forecasts future values

✅ Correct Answer: B. It processes and understands human language

Explanation:
Translation involves understanding and generating human language, making it a foundational NLP workload alongside sentiment analysis, entity recognition, and language modeling.

Go to the AI-900 Exam Prep Hub main page.

AI, AI-900, Artificial Intelligence (AI), Microsoft Certification, Natural Language Processing (NLP) January 31, 2026

Identify Features and Uses for Translation (AI-900 Exam Prep)

Where This Topic Fits in the Exam

Exam area: Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)
Sub-area: Identify features of common NLP workload scenarios
Skill focus: Recognizing when translation is the appropriate NLP workload, and understanding Azure services that support it

Translation is a core NLP workload on the AI-900 exam and often appears in short, scenario-based questions.

What Is Translation in NLP?

Translation is the process of converting text (or speech) from one language into another while preserving the original meaning.

Modern AI-powered translation systems use machine learning and deep learning models to understand context, grammar, and semantics rather than performing word-for-word substitutions.

Key Features of Translation Workloads

Translation solutions typically provide the following features:

Text-to-text translation between languages
Support for dozens of languages and dialects
Context-aware translation (not literal word replacement)
Detection of source language
Batch or real-time translation
Integration with applications, websites, and chatbots
Optional customization for domain-specific terminology

Common Uses of Translation

Translation workloads are used whenever language differences create a communication barrier.

Typical scenarios include:

Translating websites or product documentation
Supporting multilingual customer service
Translating chat messages in real time
Localizing applications for global users
Translating social media posts or reviews
Enabling communication across international teams

Azure Services for Translation

In Azure, translation capabilities are provided by:

Azure AI Translator

Azure AI Translator is part of Azure AI Services and offers:

Text translation between supported languages
Language detection
Transliteration (converting text between scripts)
Dictionary lookup and examples
Real-time and batch translation via APIs

This service uses prebuilt models, so no training is required.

Translation vs Other NLP Workloads

It is important to distinguish translation from similar NLP tasks:

NLP Task	Purpose
Translation	Convert text from one language to another
Language detection	Identify which language text is written in
Speech recognition	Convert spoken audio into text
Speech synthesis	Convert text into spoken audio
Sentiment analysis	Identify emotional tone of text

Translation and Speech

Translation workloads may involve:

Text-to-text translation (most common on AI-900)
Speech translation, which combines:
1. Speech recognition
2. Translation
3. Speech synthesis

On the exam, focus primarily on text translation scenarios, unless speech is explicitly mentioned.

Responsible AI Considerations

Translation systems should be designed with responsible AI principles in mind:

Fairness: Avoid biased or culturally inappropriate translations
Reliability: Handle idioms and context accurately
Transparency: Clearly indicate when content is machine-translated
Privacy: Protect sensitive or personal information in translated text

Exam Clues to Watch For

On AI-900, translation workloads are commonly signaled by phrases such as:

“Convert content from one language to another”
“Support multilingual users”
“Translate customer messages”
“Localize an application”

When these appear, translation is the correct NLP workload.

Key Takeaways for AI-900

Translation is an NLP workload that converts text between languages
Azure AI Translator is the primary Azure service for translation
No model training is required
Translation is different from sentiment analysis, entity recognition, and speech workloads
Exam questions are typically scenario-based and concise

Go to the Practice Exam Questions for this topic.

Go to the AI-900 Exam Prep Hub main page.

AI, AI-900, Artificial Intelligence (AI), Microsoft Certification, Natural Language Processing (NLP) January 31, 2026

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

Practice Exam Questions

Question 1

Which Azure service should you use to analyze customer reviews and determine whether the feedback is positive or negative?

A. Azure Translator
B. Azure AI Vision
C. Azure AI Language
D. Azure Speech

Correct Answer: C

Explanation:
Sentiment analysis is a text analytics capability, which is provided by the Azure AI Language service. Translator is for language conversion, Vision is for images, and Speech is for audio.

Question 2

You want to extract people, organizations, and locations from text documents. Which Azure AI Language capability should you use?

A. Key phrase extraction
B. Named entity recognition
C. Text classification
D. Language detection

Correct Answer: B

Explanation:
Named Entity Recognition (NER) identifies and categorizes entities such as people, organizations, and locations within text.

Question 3

A company wants to automatically identify the main topics discussed in customer feedback emails. Which Azure AI Language feature should be used?

A. Sentiment analysis
B. Entity recognition
C. Key phrase extraction
D. Language detection

Correct Answer: C

Explanation:
Key phrase extraction identifies the most important concepts or talking points in text, making it ideal for summarizing feedback.

Question 4

Which scenario is best suited for the Azure AI Language service?

A. Converting spoken audio into text
B. Translating documents from English to French
C. Analyzing written text for sentiment and meaning
D. Detecting objects in images

Correct Answer: C

Explanation:
The Azure AI Language service specializes in understanding and analyzing text, including sentiment, entities, and key phrases.

Question 5

Which capability of Azure AI Language allows an application to answer natural language questions based on provided documents or FAQs?

A. Sentiment analysis
B. Question answering
C. Key phrase extraction
D. Language detection

Correct Answer: B

Explanation:
Question answering enables applications and chatbots to respond to user questions using structured knowledge sources.

Question 6

A multilingual application needs to determine the language of user-submitted text before processing it further. Which Azure AI Language feature should be used?

A. Translation
B. Language detection
C. Text classification
D. Entity recognition

Correct Answer: B

Explanation:
Language detection identifies the language of input text and is often used before other NLP operations.

Question 7

Which Azure service combines sentiment analysis, entity recognition, and key phrase extraction into a single offering?

A. Azure Translator
B. Azure Speech
C. Azure AI Vision
D. Azure AI Language

Correct Answer: D

Explanation:
The Azure AI Language service provides multiple NLP capabilities under one unified service.

Question 8

You want to categorize incoming support tickets into “Billing,” “Technical,” or “General.” Which Azure AI Language capability should you use?

A. Sentiment analysis
B. Key phrase extraction
C. Text classification
D. Language detection

Correct Answer: C

Explanation:
Text classification assigns predefined categories or labels to text content.

Question 9

Which statement best describes the Azure AI Language service?

A. It focuses only on speech-to-text conversion
B. It translates text between languages
C. It extracts meaning and insights from text
D. It analyzes images and video streams

Correct Answer: C

Explanation:
Azure AI Language is designed to analyze text and extract meaning, including sentiment, entities, and key concepts.

Question 10

Which task would NOT typically be handled by the Azure AI Language service?

A. Identifying sentiment in customer reviews
B. Extracting organizations from news articles
C. Translating text from Spanish to English
D. Detecting key phrases in feedback forms

Correct Answer: C

Explanation:
Text translation is handled by Azure Translator, not the Azure AI Language service. The other tasks are core Language service capabilities.

Final Exam Tips for This Topic

If the question involves understanding text, think Azure AI Language
If the question involves translation, think Azure Translator
If the question involves speech, think Azure Speech
AI-900 questions focus on what the service does, not how to code it

Go to the AI-900 Exam Prep Hub main page.

AI, AI-900, Artificial Intelligence (AI), Microsoft Certification, Natural Language Processing (NLP) January 31, 2026

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.

Service	Primary Purpose
Azure AI Language	Understand and analyze text
Azure Translator	Translate text between languages
Azure Speech	Speech-to-text and text-to-speech
Azure Vision	Analyze 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

Go to the Practice Exam Questions for this topic.

Go to the AI-900 Exam Prep Hub main page.

AI, AI-900, Artificial Intelligence (AI), Microsoft Certification, Natural Language Processing (NLP) January 31, 2026

Describe Capabilities of the Azure AI Speech 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

For AI-900, Microsoft expects you to understand what the Azure AI Speech service does, when to use it, and how it differs from other AI services — not how to code it.

What Is the Azure AI Speech Service?

The Azure AI Speech service is a cloud-based service that enables applications to process spoken language. It allows systems to:

Convert speech into text
Convert text into natural-sounding speech
Translate spoken language
Recognize speakers and voices

It is part of Azure AI Services and focuses on audio and voice-based NLP workloads.

Core Capabilities of Azure AI Speech

1. Speech to Text

Speech to Text converts spoken audio into written text.

Key features:

Real-time transcription
Batch transcription of audio files
Support for multiple languages
Automatic punctuation and formatting

Common use cases:

Transcribing meetings or calls
Voice-controlled applications
Call center analytics
Accessibility tools (captions and subtitles)

📌 AI-900 exam tip:
If the question mentions converting spoken words into text, the answer is Azure AI Speech (Speech to Text).

2. Text to Speech

Text to Speech converts written text into natural-sounding spoken audio.

Key features:

Neural voices that sound human-like
Multiple languages and accents
Adjustable pitch, speed, and tone
Support for voice styles (e.g., cheerful, calm)

Common use cases:

Voice assistants
Read-aloud applications
Accessibility for visually impaired users
Automated announcements

📌 AI-900 exam tip:
If the scenario describes reading text out loud, think Text to Speech.

3. Speech Translation

Speech Translation converts spoken language into another language, either as text or synthesized speech.

Key features:

Real-time speech translation
Multi-language support
Can output translated speech or text

Common use cases:

Multilingual meetings
Travel and tourism apps
International customer support

📌 AI-900 exam tip:
Speech translation handles spoken language, while Azure Translator handles written text.

4. Speaker Recognition

Speaker Recognition identifies or verifies who is speaking based on their voice.

Capabilities include:

Speaker verification (confirming identity)
Speaker identification (determining who is speaking)

Common use cases:

Secure voice authentication
Call center speaker tracking
Personalized voice experiences

📌 AI-900 note:
You only need to understand what it does, not how voice models are trained.

5. Speech-to-Speech Scenarios

By combining Speech to Text, Translation, and Text to Speech, Azure AI Speech supports end-to-end voice experiences, such as:

Speaking in one language and hearing a response in another
Voice-based chatbots
Smart devices and assistants

How Azure AI Speech Differs from Other Azure AI Services

Service	Primary Purpose
Azure AI Speech	Spoken language (audio)
Azure AI Language	Written text analysis
Azure Translator	Text translation
Azure AI Vision	Images and video

📌 Exam pattern to watch for:
Microsoft often tests whether you can choose the right service based on the input type (audio vs text vs image).

Typical AI-900 Scenarios Involving Azure AI Speech

You should choose Azure AI Speech when a scenario involves:

Audio recordings
Live speech
Voice input or output
Real-time transcription
Spoken translation

Key Takeaways for the AI-900 Exam

Azure AI Speech focuses on spoken language, not written text
Core capabilities:
- Speech to Text
- Text to Speech
- Speech Translation
- Speaker Recognition
Exam questions are scenario-based, not technical
If the question mentions audio, voice, or speech, Azure AI Speech is usually the answer