Tag: Microsoft Certified: Azure AI Fundamentals

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

Identify Features and Uses for Key Phrase Extraction (AI-900 Exam Prep)

Overview

Key phrase extraction is a Natural Language Processing (NLP) capability that identifies the main topics or important terms within unstructured text. In the context of the AI-900: Microsoft Azure AI Fundamentals exam, you are expected to understand what key phrase extraction does, when to use it, and how it differs from other NLP workloads.

In Azure, key phrase extraction is provided through Azure AI Language using prebuilt models, requiring no custom training.

What Is Key Phrase Extraction?

Key phrase extraction answers the question:

“What is this text mainly about?”

It analyzes text and returns a list of relevant words or short phrases that summarize the core ideas.

Example:

“Azure AI provides cloud-based artificial intelligence services for developers.”

Extracted key phrases might include:

Azure AI
artificial intelligence services
cloud-based
developers

Core Features of Key Phrase Extraction

1. Automatic Topic Identification

The service automatically identifies:

Important concepts
Repeated or emphasized terms
Meaningful noun phrases

This helps users quickly understand large volumes of text.

2. Works with Unstructured Text

Key phrase extraction can be applied to:

Customer reviews
Support tickets
Emails
Social media posts
Articles and documents

No formatting or labeling is required.

3. Prebuilt NLP Models

For AI-900 purposes:

No model training is required
No labeled datasets are needed
The service is accessed via API calls or SDKs

This makes it ideal for rapid implementation.

4. Multi-Language Support

Azure AI Language supports multiple languages for key phrase extraction, making it suitable for global applications.

Common Use Cases

Summarizing Customer Feedback

Organizations can extract key phrases from thousands of customer comments to identify:

Common complaints
Popular features
Emerging issues

Search and Indexing

Key phrases can be used to:

Improve document search
Tag content automatically
Enhance content discoverability

Trend and Topic Analysis

By aggregating extracted phrases, businesses can:

Identify trending topics
Monitor brand mentions
Analyze public sentiment themes

Key Phrase Extraction vs Other NLP Workloads

NLP Capability	Primary Purpose
Key phrase extraction	Identify main topics in text
Sentiment analysis	Determine emotional tone
Language detection	Identify the language used
Entity recognition	Extract specific entities (names, dates, locations)

Understanding these distinctions is critical for AI-900 exam questions.

Typical AI-900 Exam Scenarios

You may see questions describing:

Analyzing large amounts of feedback text
Automatically tagging documents
Identifying main discussion points without understanding emotion

Correct answers will reference:

Key phrase extraction
Azure AI Language
Prebuilt NLP models

Responsible AI Considerations

Although key phrase extraction does not directly analyze people, responsible usage still includes:

Avoiding misinterpretation of extracted phrases
Understanding that output is contextual, not definitive
Using extracted phrases as decision support, not final judgment

Key Takeaways for the AI-900 Exam

Key phrase extraction identifies important topics, not sentiment
It works on unstructured text
It uses pretrained models in Azure AI Language
It complements other NLP workloads rather than replacing them

A strong grasp of when to use key phrase extraction will help you confidently answer AI-900 questions related to Natural Language Processing workloads.

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 entity recognition (AI-900 Exam Prep)

Practice Questions

Question 1

What is the primary purpose of entity recognition in Natural Language Processing?

A. Determine the sentiment of text
B. Identify important topics in a document
C. Extract specific real-world items from text
D. Translate text between languages

Correct Answer: C

Explanation:
Entity recognition focuses on identifying specific real-world entities such as people, organizations, locations, dates, and numbers—not sentiment, topics, or translations.

Question 2

Which Azure service provides entity recognition capabilities?

A. Azure Machine Learning
B. Azure AI Language
C. Azure AI Vision
D. Azure Cognitive Search

Correct Answer: B

Explanation:
Entity recognition is part of Azure AI Language, which provides NLP capabilities such as entity recognition, sentiment analysis, and key phrase extraction.

Question 3

A company wants to extract names, dates, and organization names from customer emails. Which NLP capability should be used?

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

Correct Answer: D

Explanation:
Entity recognition is designed to extract specific items like names, dates, and organizations from unstructured text.

Question 4

Which of the following is an example of an entity?

A. “Customer feedback”
B. “Very satisfied”
C. “Microsoft”
D. “Fast delivery”

Correct Answer: C

Explanation:
“Microsoft” is a named organization, which qualifies as an entity. The other options describe concepts or sentiments, not entities.

Question 5

What type of entity recognition is used to identify email addresses and phone numbers?

A. Key phrase extraction
B. Language detection
C. Personally Identifiable Information (PII) detection
D. Sentiment analysis

Correct Answer: C

Explanation:
PII detection is a form of entity recognition that identifies sensitive personal data, such as phone numbers and email addresses.

Question 6

Which scenario is the best use case for entity recognition?

A. Summarizing long documents
B. Translating text into different languages
C. Flagging sensitive data in support tickets
D. Measuring customer satisfaction

Correct Answer: C

Explanation:
Entity recognition (specifically PII detection) is commonly used to identify sensitive data for compliance and security purposes.

Question 7

Does entity recognition in Azure AI Language require you to train a custom machine learning model?

A. Yes, always
B. Yes, but only for large datasets
C. No, it uses pretrained models
D. Only if multiple languages are involved

Correct Answer: C

Explanation:
For AI-900, entity recognition uses prebuilt, pretrained models, requiring no ML expertise or training.

Question 8

Which feature distinguishes entity recognition from key phrase extraction?

A. Entity recognition identifies emotions
B. Entity recognition detects specific real-world items
C. Entity recognition summarizes text
D. Entity recognition translates languages

Correct Answer: B

Explanation:
Entity recognition identifies specific, concrete items (names, places, dates), while key phrase extraction identifies important topics or concepts.

Question 9

A document contains the sentence:

“The meeting is scheduled for March 10 in Seattle.”

Which entities could be identified?

A. Sentiment and key phrases
B. A date and a location
C. An organization and a person
D. A language and a topic

Correct Answer: B

Explanation:
“March 10” is a date, and “Seattle” is a location, both of which are standard entity types.

Question 10

Which statement about entity recognition is true?

A. It only works with structured data
B. It requires labeled training data
C. It identifies specific items within unstructured text
D. It is used only for translation tasks

Correct Answer: C

Explanation:
Entity recognition analyzes unstructured text to identify specific, meaningful items, such as names, places, and dates.

Final Exam Takeaways

Entity recognition answers “Who, what, where, and when?”
It is provided by Azure AI Language
No model training is required
Commonly tested alongside key phrase extraction and sentiment analysis
PII detection is a key real-world use case

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 Entity Recognition (AI-900 Exam Prep)

Where this fits in the exam

Exam domain: Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)
Sub-area: Identify features of common NLP workload scenarios
Key skill tested: Understanding what entity recognition is, what it’s used for, and which Azure service provides it

You are not expected to build or train models—only to recognize capabilities and use cases.

What Is Entity Recognition?

Entity recognition (also called Named Entity Recognition or NER) is an NLP capability that identifies and categorizes specific, real-world items mentioned in text.

These items (entities) typically fall into predefined categories such as:

People
Organizations
Locations
Dates and times
Numbers
Products
Email addresses, phone numbers, URLs

Simple example

Input text:

“Satya Nadella is the CEO of Microsoft, headquartered in Redmond.”

Extracted entities:

Person: Satya Nadella
Organization: Microsoft
Location: Redmond

Azure Service That Provides Entity Recognition

Entity recognition is provided by Azure AI Language, part of Azure’s AI services portfolio.

Key points for the exam:

Uses prebuilt models
No machine learning expertise required
Accessed via REST APIs or SDKs
Supports multiple languages

Types of Entity Recognition in Azure AI Language

For AI-900, you mainly need to understand the concept, but it helps to know the types at a high level.

1. Named Entity Recognition

Identifies common entity categories, such as:

Person
Location
Organization
Date
Quantity

2. Personally Identifiable Information (PII) Detection

Detects sensitive personal data, including:

Phone numbers
Email addresses
Social security numbers
Credit card numbers

This is often tested conceptually in the context of compliance and data privacy.

Common Use Cases for Entity Recognition

1. Information Extraction

Automatically pull important data from unstructured text such as:

Contracts
Emails
Reports
Support tickets

2. Search and Indexing

Improve search by identifying names, locations, or products mentioned in documents.

3. Data Classification and Tagging

Label documents based on recognized entities to:

Route support tickets
Organize content
Trigger workflows

4. Compliance and Security

Detect and flag PII to:

Prevent data leaks
Meet regulatory requirements
Mask sensitive data

Entity Recognition vs Other NLP Capabilities

This comparison is very exam-relevant.

Capability	What it identifies
Entity recognition	Specific items (names, places, dates)
Key phrase extraction	Main topics and concepts
Sentiment analysis	Emotional tone
Language detection	Language of the text

If the question asks “Who, where, or what specifically?” → entity recognition
If it asks “What is this text about?” → key phrase extraction

Key Features to Remember for the Exam

Uses pretrained models
Works with unstructured text
Supports multiple languages
Does not require labeled training data
Commonly used for information extraction and compliance

Responsible AI Considerations

Microsoft emphasizes responsible AI even at the fundamentals level.

Important considerations:

Entity recognition may misidentify entities due to ambiguity
Results should be reviewed before being used for critical decisions
Sensitive data detection should align with privacy and compliance policies

Exam Tips

Expect scenario-based questions, not code
Focus on matching the right NLP capability to the scenario
Look for keywords like:
- names, addresses, dates, organizations → Entity recognition
- topics, summaries → Key phrase extraction
- opinions, feelings → Sentiment analysis

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 sentiment analysis (AI-900 Exam Prep)

Practice Questions

Question 1

What is the primary purpose of sentiment analysis in Natural Language Processing?

A. To identify people, places, and organizations in text
B. To determine the emotional tone of text
C. To translate text between languages
D. To summarize large documents

Correct Answer: B

Explanation:
Sentiment analysis evaluates the emotional tone or opinion expressed in text, such as positive, negative, neutral, or mixed. Entity recognition, translation, and summarization are different NLP tasks.

Question 2

Which Azure service provides sentiment analysis capabilities?

A. Azure Machine Learning
B. Azure AI Vision
C. Azure AI Language
D. Azure Cognitive Search

Correct Answer: C

Explanation:
Sentiment analysis is part of Azure AI Language, which provides pretrained NLP models for analyzing text sentiment, key phrases, entities, and more.

Question 3

A company wants to analyze customer reviews to determine whether feedback is positive or negative. Which AI capability should they use?

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

Correct Answer: B

Explanation:
Sentiment analysis is designed to classify text based on emotional tone, making it ideal for customer reviews and feedback analysis.

Question 4

Which sentiment classifications can Azure AI Language return?

A. Happy, Sad, Angry
B. Positive, Negative, Neutral, Mixed
C. True, False, Unknown
D. Approved, Rejected, Pending

Correct Answer: B

Explanation:
Azure sentiment analysis classifies text into positive, negative, neutral, or mixed sentiments.

Question 5

Which additional information is returned with sentiment analysis results?

A. Translation accuracy
B. Confidence scores
C. Named entities
D. Text summaries

Correct Answer: B

Explanation:
Sentiment analysis includes confidence scores, indicating how strongly the model believes the sentiment classification applies.

Question 6

A support team wants to automatically identify angry customer emails for escalation. Which NLP feature is most appropriate?

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

Correct Answer: C

Explanation:
Sentiment analysis helps detect negative or frustrated emotions, enabling automated prioritization of customer support requests.

Question 7

Which scenario is NOT an appropriate use case for sentiment analysis?

A. Measuring public opinion on social media
B. Identifying dissatisfaction in survey responses
C. Extracting product names from reviews
D. Monitoring brand perception

Correct Answer: C

Explanation:
Extracting product names is a task for entity recognition, not sentiment analysis.

Question 8

Does sentiment analysis in Azure AI Language require custom model training?

A. Yes, labeled data is required
B. Yes, but only for large datasets
C. No, it uses pretrained models
D. Only when using multiple languages

Correct Answer: C

Explanation:
Azure AI Language uses pretrained models, allowing sentiment analysis without building or training custom machine learning models.

Question 9

At which levels can sentiment analysis be applied?

A. Document level only
B. Sentence level only
C. Word level only
D. Document and sentence level

Correct Answer: D

Explanation:
Azure sentiment analysis evaluates sentiment at both the document level and sentence level, allowing more detailed insights.

Question 10

A business wants to understand how customers feel about a product, not what the product is. Which NLP capability should be used?

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

Correct Answer: C

Explanation:
Sentiment analysis focuses on emotional tone, while key phrase extraction and entity recognition focus on content and structure.

Final Exam Tip 🎯

For AI-900, always ask yourself:

“Am I being asked about emotion or opinion?”

If the answer is yes → Sentiment analysis

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 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 Language Modeling (AI-900 Exam Prep)

Overview

Language modeling is a core concept in Natural Language Processing (NLP) that focuses on enabling machines to understand, generate, and predict human language. In the context of the AI-900 exam, language modeling is not about building models from scratch, but about recognizing what language models do, what problems they solve, and how Azure provides access to them.

Language models power many modern AI experiences, including chatbots, text generation, summarization, translation, and question answering.

What Is a Language Model?

A language model is a type of AI model that learns patterns in language so it can:

Predict the next word or token in a sequence
Understand context and meaning
Generate coherent and contextually relevant text

At a fundamental level, language models calculate the probability of word sequences, which allows them to both interpret and generate language.

Key Features of Language Modeling

1. Text Prediction and Generation

Language models can:

Predict the next word in a sentence
Generate full sentences, paragraphs, or documents
Produce human-like responses in conversations

Example:

“The weather today is very…” → sunny

2. Context Awareness

Modern language models (especially transformer-based models) consider context, not just individual words.

This allows them to:

Understand sentence meaning
Maintain coherence across multiple sentences
Respond appropriately based on prior text

3. Natural Language Understanding and Generation

Language models support both:

Understanding text (reading and interpreting meaning)
Generating text (writing responses, summaries, or explanations)

This dual capability is central to many NLP workloads.

4. Pretrained Models

In Azure, language modeling typically relies on pretrained models, meaning:

No custom training is required
Models are already trained on large text datasets
Users can immediately apply them to common NLP tasks

This aligns with the AI-900 focus on consuming AI services, not building models.

Common Uses of Language Modeling

1. Chatbots and Virtual Assistants

Language models enable conversational AI by:

Understanding user input
Generating natural responses
Maintaining conversation context

Azure Example:
Chatbots built using Azure OpenAI Service or language-based Azure AI services.

2. Text Completion and Content Generation

Language models can:

Auto-complete sentences
Generate emails, reports, or documentation
Assist with creative writing or code comments

3. Question Answering

Language models can:

Interpret natural language questions
Generate relevant answers based on context or provided data

This is commonly used in:

Help desks
Knowledge bases
Internal support tools

4. Text Summarization

Language models can:

Condense long documents
Extract key points
Provide concise summaries

This helps users quickly understand large volumes of text.

5. Language Translation and Adaptation

While translation is often a separate NLP workload, language models:

Understand sentence structure
Preserve meaning across languages
Adapt phrasing naturally

Language Modeling in Azure

In Azure, language modeling capabilities are available through services such as:

Azure OpenAI Service

Provides access to powerful large language models
Supports text generation, chat, summarization, and reasoning tasks
Uses pretrained transformer-based models

Azure AI Language

Focuses on structured NLP tasks
Complements language modeling with features like sentiment analysis and entity recognition

For AI-900, it’s important to recognize what language models enable, not the underlying implementation details.

Language Modeling vs Other NLP Tasks (Exam Tip)

NLP Task	Focus
Sentiment analysis	Emotional tone
Entity recognition	Identifying names, places, organizations
Key phrase extraction	Important terms
Language modeling	Understanding and generating language

If the question involves predicting, generating, or responding with text, language modeling is likely the correct concept.

Why Language Modeling Matters for AI-900

Microsoft includes language modeling in AI-900 to ensure candidates understand:

How modern AI systems interact with human language
Why conversational AI is possible
How Azure provides ready-to-use NLP capabilities

You are not expected to train models — only to identify features, uses, and scenarios.

Exam Takeaway

If a question mentions:

Text generation
Conversational AI
Predicting words or sentences
Understanding context in language

👉 Think Language Modeling

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 Speech Recognition and Synthesis (AI-900 Exam Prep)

Practice Questions

Question 1

A company wants to convert recorded customer support calls into written transcripts for analysis.
Which NLP workload is required?

A. Speech synthesis
B. Language modeling
C. Speech recognition
D. Text translation

Correct Answer: C

Explanation:
Speech recognition converts spoken audio into text. Transcribing recorded calls is a classic speech recognition scenario.

Question 2

An application reads incoming emails aloud to visually impaired users.
Which capability does this require?

A. Speech recognition
B. Speech synthesis
C. Key phrase extraction
D. Sentiment analysis

Correct Answer: B

Explanation:
Speech synthesis converts text into spoken audio, making it ideal for reading text aloud.

Question 3

Which Azure service provides both speech-to-text and text-to-speech capabilities?

A. Azure AI Language
B. Azure AI Vision
C. Azure AI Speech
D. Azure Machine Learning

Correct Answer: C

Explanation:
Azure AI Speech supports both speech recognition (speech-to-text) and speech synthesis (text-to-speech).

Question 4

A voice-controlled virtual assistant must understand spoken commands from users.
Which NLP workload does this scenario require?

A. Text analytics
B. Speech synthesis
C. Speech recognition
D. Language translation

Correct Answer: C

Explanation:
Understanding spoken commands requires converting speech into text, which is speech recognition.

Question 5

A chatbot responds verbally to users after processing their requests.
Which capability enables the chatbot to speak its responses?

A. Speech recognition
B. Speech synthesis
C. Entity recognition
D. Language detection

Correct Answer: B

Explanation:
Speech synthesis generates spoken audio from text, enabling verbal responses.

Question 6

Which input and output combination correctly describes speech recognition?

A. Text input → Audio output
B. Audio input → Text output
C. Text input → Text output
D. Audio input → Audio output

Correct Answer: B

Explanation:
Speech recognition takes audio input and produces text output.

Question 7

Which scenario uses both speech recognition and speech synthesis?

A. Extracting key phrases from a document
B. Translating text from English to Spanish
C. A voice assistant that listens and responds verbally
D. Analyzing customer sentiment in reviews

Correct Answer: C

Explanation:
A voice assistant listens (speech recognition) and speaks back (speech synthesis), using both capabilities together.

Question 8

A system generates natural-sounding voices with adjustable pitch and speed.
Which technology is being used?

A. Speech recognition
B. Language modeling
C. Speech synthesis
D. Optical character recognition

Correct Answer: C

Explanation:
Speech synthesis creates spoken audio and can adjust voice characteristics such as pitch and speed.

Question 9

Which phrase in a question most strongly indicates a speech recognition workload?

A. “Identify important terms in a document”
B. “Analyze the emotional tone of text”
C. “Convert spoken instructions into written commands”
D. “Generate audio from text responses”

Correct Answer: C

Explanation:
Converting spoken instructions into text is speech recognition.

Question 10

Which Azure NLP workload is most appropriate for real-time meeting transcription?

A. Speech synthesis
B. Speech recognition
C. Entity recognition
D. Language detection

Correct Answer: B

Explanation:
Real-time transcription requires converting live audio into text, which is speech recognition.

Final Exam Tips

Speech → Text = Speech recognition
Text → Speech = Speech synthesis
Voice assistants usually require both
Azure service to remember: Azure AI Speech
Watch for keywords like:
- Transcribe, dictate, spoken commands → Recognition
- Read aloud, generate voice, spoken response → Synthesis

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.