---
title: "AI-900 Study Guide — Microsoft Azure AI Fundamentals"
description: "An interactive study guide built on 7 memory techniques to help you pass the Microsoft Azure AI Fundamentals exam."
url: "https://lucidlabs.com.au/insights/ai-900"
---

# Master the AI-900

An interactive study guide built on 7 memory techniques to help you pass the Microsoft Azure AI Fundamentals exam.

AI Workloads 15-20%Machine Learning 20-25%Computer Vision 15-20%NLP 15-20%Generative AI 15-20%

What it covers

AI concepts, responsible AI principles, machine learning (regression, classification, clustering), Azure AI Vision, Azure AI Language, Azure OpenAI Service, and Microsoft Copilot.

Ideal for

IT professionals, business decision makers, students, and anyone exploring artificial intelligence and machine learning on Azure.

Aspire to this if

You're curious about AI, want to validate your understanding of Azure AI services, or plan to pursue associate-level AI or data science certifications.

Section 1 / Spatial Memory

## The Map

Tap any component to see what you need to know for the exam.

**🤖 AI Workloads**

Prediction · Anomaly · Vision · NLP

**⚖️ Responsible AI**

Fairness · Reliability · Privacy

**🧠 ML Fundamentals**

Train · Validate · Evaluate

**📈 Regression**

Predict Numbers

**🏷️ Classification**

Predict Categories

**🔵 Clustering**

Find Groups

**👁️ Computer Vision**

Azure AI Vision · Custom Vision · Face

**💬 Natural Language Processing**

Azure AI Language · Speech · Translator

**✨ Generative AI**

Azure OpenAI · GPT · DALL-E · Copilot

**☁️ Azure AI Services**

APIs · Studio · Deployment

**🔬 Azure Machine Learning**

AutoML · Designer · Notebooks

**🛡️ AI Safety & Governance**

Content Filters · Grounding · Red Teaming

Section 2 / Narrative Memory

## The Story

Follow the narrative to build a mental model of how everything connects.

🤖

### The Spark of Intelligence

Artificial intelligence begins with a simple idea — machines that mimic human cognition. AI workloads span prediction (what will happen?), anomaly detection (what’s unusual?), computer vision (what do I see?), NLP (what does this mean?), and generative AI (create something new). Each workload maps to specific Azure services.

**Exam Intel**AI workload types: prediction, anomaly detection, computer vision, NLP, conversational AI, generative AI. Know which Azure service handles each. AI services are consumed as APIs, not built from scratch.

⚖️

### The Ethical Compass

Microsoft’s six Responsible AI principles guide every AI deployment. Fairness ensures no group is disadvantaged. Reliability demands consistent, safe performance. Privacy protects personal data. Inclusiveness designs for everyone. Transparency explains decisions. Accountability keeps humans in control.

**Exam Intel**Memorise all 6 principles: Fairness, Reliability & Safety, Privacy & Security, Inclusiveness, Transparency, Accountability. Expect questions that describe a scenario and ask which principle applies. Accountability = humans are responsible, not the AI.

🧠

### The Learning Machine

Machine learning is the engine powering AI. Models learn from data through training, validation, and testing. Supervised learning uses labelled data (regression, classification). Unsupervised learning discovers hidden patterns (clustering). The features are what goes in; the label is what comes out.

**Exam Intel**Supervised = labelled data (regression + classification). Unsupervised = no labels (clustering). Features = input columns. Label = predicted value. Training/validation/test split prevents overfitting. Know the difference between the three ML types.

📈

### The Numbers Game

Regression predicts continuous values — house prices, temperatures, revenue. Classification sorts into categories — spam vs. legitimate, approved vs. denied. Clustering groups unlabelled data into natural segments. Each approach uses different algorithms and different evaluation metrics.

**Exam Intel**Regression metrics: MAE, RMSE, R². Classification metrics: accuracy, precision, recall, F1, AUC. Clustering: K-Means assigns to K groups. Confusion matrix: TP, FP, TN, FN. Binary classification = 2 classes, multi-class = 3+.

👁️

### The Eyes of AI

Computer vision gives machines the power of sight. Azure AI Vision analyses images, extracts text via OCR, and generates captions. Custom Vision lets you train classifiers and object detectors with just a handful of images. Face API detects, identifies, and verifies faces — with strict ethical guidelines.

**Exam Intel**Image classification = label the whole image. Object detection = bounding boxes + labels. OCR = extract printed/handwritten text. Custom Vision: upload images → tag → train → predict. Face API: detection (where), identification (who), verification (confirm).

💬

### The Language Bridge

Natural language processing helps AI understand and generate human language. Azure AI Language extracts sentiment, key phrases, and named entities. Azure AI Speech converts spoken words to text and back again. Conversational Language Understanding (CLU) powers chatbots by recognising intents and entities.

**Exam Intel**NLP capabilities: sentiment analysis, key phrases, NER, PII detection, language detection, question answering. CLU: intents (what the user wants) + entities (key details). Speech: STT, TTS, translation. Translator: 100+ languages, real-time.

✨

### The Generative Revolution

Generative AI creates new content — text, images, code. Azure OpenAI Service provides enterprise access to GPT models, DALL-E for images, and Whisper for speech. Prompt engineering shapes the output. RAG (Retrieval-Augmented Generation) grounds responses in your own data to reduce hallucinations.

**Exam Intel**LLMs predict the next token. Temperature controls randomness (low = deterministic, high = creative). Prompt engineering: system message, few-shot examples, clear instructions. RAG = search your data + feed to LLM. DALL-E generates images from text prompts.

☁️

### The Azure AI Toolbox

Azure AI services are the building blocks. Pre-built APIs handle common tasks (vision, language, speech). Azure AI Studio unifies the experience for building custom AI. Azure Machine Learning provides the full pipeline — AutoML for quick starts, Designer for no-code, and notebooks for data scientists.

**Exam Intel**Azure AI services = pre-built APIs (key + endpoint). Azure Machine Learning = full ML platform. AutoML = automated algorithm selection. Designer = drag-and-drop. AI Studio = unified portal for generative AI. All secured with keys or Microsoft Entra ID.

🤝

### The Copilot Era

Microsoft Copilot brings AI to every product. Built on top of large language models, grounded in your organisational data via Microsoft Graph. Copilot in Microsoft 365 helps write documents, summarise emails, and create presentations. It’s the consumer-facing expression of the AI platform underneath.

**Exam Intel**Copilot = LLM + Microsoft Graph + your data. Microsoft 365 Copilot: Word, Excel, PowerPoint, Teams, Outlook. Bing Chat (Copilot): web search + LLM. Copilot Studio: build custom copilots. Grounding in organisational data is the key differentiator.

🛡️

### The Safety Net

AI must be safe before it can be trusted. Azure AI Content Safety detects harmful text and images across four categories. Content filters in Azure OpenAI apply configurable severity thresholds. Red teaming stress-tests models. The Responsible AI dashboard provides fairness analysis, error analysis, and model interpretability.

**Exam Intel**Content Safety: 4 categories — violence, hate, sexual, self-harm. Each rated severity 0–6. Azure OpenAI content filters are on by default. Grounding reduces hallucinations. Red teaming = adversarial testing. Responsible AI dashboard = fairness + error analysis + interpretability.

Section 3 / Acronym Memory

## Mnemonic Wall

Memorable acronyms and phrases to anchor key exam concepts in your memory.

⚖️

FRIPT​A

**F**airness, **R**eliability & Safety, **I**nclusiveness, **P**rivacy & Security, **T**ransparency, **A**ccountability

Microsoft’s 6 Responsible AI principles. Expect scenario questions — match the described issue to the correct principle.

🧠

SUS

**S**upervised, **U**nsupervised, Reinforcement (**S**elf-learning)

Three ML paradigms. Supervised = labelled data. Unsupervised = no labels. Reinforcement = reward-based. AI-900 focuses on supervised and unsupervised.

📈

RCC

**R**egression, **C**lassification, **C**lustering

Three core ML task types. Regression = numbers. Classification = categories. Clustering = groups. Know which is supervised vs unsupervised.

📊

MAR

**M**AE, **A**bsolute, **R**²

Regression metrics: MAE (average error), RMSE (penalises large errors), R² (0–1, closer to 1 = better fit).

🏷️

PRFA

**P**recision, **R**ecall, **F**1, **A**UC

Classification metrics. Precision = correct positives / all predicted positives. Recall = correct positives / all actual positives. F1 = harmonic mean. AUC = area under ROC curve.

👁️

COF

**C**lassification, **O**bject detection, **F**ace

Three Azure AI Vision capabilities. Classification = label whole image. Object detection = bounding boxes. Face = detect/identify/verify.

💬

SKEP

**S**entiment, **K**ey phrases, **E**ntities, **P**II

Core Azure AI Language capabilities. Also includes language detection and question answering. NER recognises people, places, organisations.

🎤

STT-TTS

**S**peech-**T**o-**T**ext / **T**ext-**T**o-**S**peech

Azure AI Speech bidirectional capabilities. STT = transcription. TTS = synthesis. Also supports real-time speech translation.

🤖

CLU

**C**onversational **L**anguage **U**nderstanding

Build chatbots by defining intents (what user wants) and entities (key details). Replaces LUIS. Trained with example utterances.

✨

GDW

**G**PT, **D**ALL-E, **W**hisper

Azure OpenAI models. GPT = text generation. DALL-E = image generation. Whisper = speech-to-text. All accessed via Azure OpenAI Service.

📝

RAG

**R**etrieval-**A**ugmented **G**eneration

Ground LLM responses in your own data. Search relevant docs → inject into prompt → generate grounded answer. Reduces hallucinations.

🎯

TEMP

**T**emperature **E**ffects **M**odel **P**redictability

Temperature parameter: low (0) = deterministic, focused. High (1+) = creative, random. Controls randomness of token selection.

🛡️

VHSS

**V**iolence, **H**ate, **S**exual, **S**elf-harm

Four Azure AI Content Safety categories. Each rated by severity (0–6). Content filters on Azure OpenAI are enabled by default.

🔬

ADN

**A**utoML, **D**esigner, **N**otebooks

Three ways to build ML in Azure Machine Learning. AutoML = automated. Designer = drag-and-drop. Notebooks = code-first (Python/R).

🤝

LMG

**L**LM + **M**icrosoft Graph + **G**rounding

How Copilot works: LLM for reasoning, Microsoft Graph for organisational data, grounding for accuracy. This triad powers all Copilot experiences.

Section 4 / Contrast Memory

## Versus Arena

Side-by-side comparisons to sharpen your understanding of similar concepts.

vs

RegressionvsClassification

Click to compare

#### Regression vs Classification

| Aspect | Regression | Classification |
| --- | --- | --- |
| Predicts | Continuous number | Category/class label |
| Output | Price, temperature, score | Spam/not spam, yes/no |
| Learning | Supervised | Supervised |
| Metrics | MAE, RMSE, R² | Accuracy, Precision, Recall, F1, AUC |
| Algorithms | Linear regression, decision forest | Logistic regression, decision tree |
| Example | Predict house price | Predict loan approval (yes/no) |

Click to flip back

vs

SupervisedvsUnsupervised

Click to compare

#### Supervised vs Unsupervised

| Aspect | Supervised | Unsupervised |
| --- | --- | --- |
| Labels | Requires labelled data | No labels needed |
| Goal | Predict outcomes | Discover patterns/groups |
| Types | Regression, classification | Clustering |
| Example | Predict churn (yes/no) | Segment customers into groups |
| Training data | Features + known label | Features only |
| Evaluation | Compare prediction vs actual | Cluster quality metrics |

Click to flip back

vs

Image ClassificationvsObject Detection

Click to compare

#### Image Classification vs Object Detection

| Aspect | Image Classification | Object Detection |
| --- | --- | --- |
| Output | Single label for whole image | Bounding boxes + labels |
| Answers | “What is this image?” | “What objects are where?” |
| Count | One label per image | Multiple objects detected |
| Use case | Photo categorisation | Autonomous driving, retail |
| Azure service | Custom Vision (classifier) | Custom Vision (detector) |
| Complexity | Simpler | More complex |

Click to flip back

vs

Azure AI ServicesvsAzure ML

Click to compare

#### Azure AI Services vs Azure ML

| Aspect | Azure AI Services | Azure ML |
| --- | --- | --- |
| Approach | Pre-built APIs | Build custom models |
| Skill level | Developer (no ML expertise) | Data scientist / ML engineer |
| Customisation | Limited (some fine-tuning) | Full control |
| Setup time | Minutes (API call) | Hours to weeks (training) |
| Examples | Vision, Language, Speech APIs | AutoML, Designer, Notebooks |
| Best for | Common AI tasks | Unique/complex ML problems |

Click to flip back

vs

GPTvsDALL-EvsWhisper

Click to compare

#### GPT vs DALL-E vs Whisper

| Aspect | GPT | DALL-E | Whisper |
| --- | --- | --- | --- |
| Modality | Text generation | Image generation | Speech-to-text |
| Input | Text prompt | Text prompt | Audio file |
| Output | Text/code | Images | Transcribed text |
| Use case | Chat, summarise, code | Create visuals | Transcribe meetings |
| Temperature | Controls randomness | N/A | N/A |
| Azure service | Azure OpenAI | Azure OpenAI | Azure OpenAI |

Click to flip back

vs

PrecisionvsRecall

Click to compare

#### Precision vs Recall

| Aspect | Precision | Recall |
| --- | --- | --- |
| Question | How many positives are correct? | How many actual positives found? |
| Formula | TP / (TP + FP) | TP / (TP + FN) |
| High when | Few false positives | Few false negatives |
| Prioritise when | False alarm is costly (spam filter) | Missing cases is costly (disease) |
| Trade-off | Higher precision → lower recall | Higher recall → lower precision |
| Balance | F1 score = harmonic mean of both | F1 score = harmonic mean of both |

Click to flip back

vs

CLUvsQuestion Answering

Click to compare

#### CLU vs Question Answering

| Aspect | CLU | Question Answering |
| --- | --- | --- |
| Purpose | Understand user intent | Answer from knowledge base |
| Input | User utterance | User question |
| Output | Intent + entities | Best matching answer |
| Training | Example utterances | FAQ pairs / documents |
| Use case | Chatbot command routing | FAQ bot, help desk |
| Azure feature | Conversational Language Understanding | Custom Question Answering |

Click to flip back

vs

AutoMLvsDesignervsNotebooks

Click to compare

#### AutoML vs Designer vs Notebooks

| Aspect | AutoML | Designer | Notebooks |
| --- | --- | --- | --- |
| Approach | Automated | No-code | Code-first |
| Audience | Business users | Citizen devs | Data scientists |
| Customisation | Algorithm auto-selected | Drag-and-drop | Full control |
| Speed | Fastest | Moderate | Slowest (but most flexible) |
| Output | Best model selected | Pipeline | Custom pipeline |
| Language | None | None | Python / R |

Click to flip back

vs

Content SafetyvsResponsible AI

Click to compare

#### Content Safety vs Responsible AI Principles

| Aspect | Content Safety | Responsible AI |
| --- | --- | --- |
| What | Technical service / API | Guiding principles |
| Scope | Detect harmful content | Govern all AI systems |
| Categories | Violence, hate, sexual, self-harm | Fairness, reliability, privacy, inclusiveness, transparency, accountability |
| Applied | Runtime content filtering | Design-time decisions |
| Automation | Automated severity scoring | Human-led assessment |
| Azure tool | Azure AI Content Safety | Responsible AI dashboard |

Click to flip back

Section 5 / Grouping Memory

## Cheat Sheet

Organised reference grouped by exam domain — everything you need on one page.

### AI Workloads & Responsible AI

15-20%

#### AI Workload Types

-   Prediction: forecast future values (regression, classification)
-   Anomaly detection: identify unusual patterns
-   Computer vision: analyse images and video
-   NLP: understand and generate human language
-   Conversational AI: chatbots and virtual assistants
-   Generative AI: create new content (text, images, code)

#### Responsible AI Principles

-   Fairness: treat all people equitably, avoid bias
-   Reliability & Safety: perform consistently and safely
-   Privacy & Security: protect personal data
-   Inclusiveness: empower everyone, design for all
-   Transparency: explain how AI makes decisions
-   Accountability: humans responsible for AI outcomes

#### Key Concepts

-   AI = machines mimicking human cognitive functions
-   ML = subset of AI, learning from data
-   Deep learning = subset of ML, neural networks
-   Azure AI services = pre-built APIs for common tasks

### Machine Learning Fundamentals

20-25%

#### ML Types

-   Supervised: labelled data (regression + classification)
-   Unsupervised: no labels (clustering)
-   Features = input variables, Label = target to predict
-   Training/validation/test split prevents overfitting

#### Regression

-   Predicts continuous numeric values
-   Algorithms: linear regression, decision forest
-   Metrics: MAE, RMSE, R² (closer to 1 = better)

#### Classification

-   Predicts categories (binary or multi-class)
-   Algorithms: logistic regression, decision tree, neural network
-   Metrics: accuracy, precision, recall, F1, AUC-ROC
-   Confusion matrix: TP, FP, TN, FN

#### Clustering

-   Groups similar data without labels
-   K-Means: assign data to K clusters
-   Use cases: customer segmentation, anomaly detection

#### Azure Machine Learning

-   AutoML: automatic algorithm and hyperparameter selection
-   Designer: drag-and-drop pipeline builder
-   Notebooks: code-first with Python/R
-   MLflow for experiment tracking and model registry

### Computer Vision

15-20%

#### Azure AI Vision

-   Image analysis: tags, captions, categories, objects
-   OCR: extract printed and handwritten text
-   Spatial analysis: people counting, movement tracking
-   Smart cropping: intelligent image thumbnails

#### Custom Vision

-   Image classification: label the whole image
-   Object detection: bounding boxes + labels
-   Train with small datasets (as few as 5 images per tag)
-   Export models to edge devices (ONNX, TensorFlow)

#### Face API

-   Detection: locate faces, return attributes
-   Identification: match face to known person
-   Verification: confirm two faces are same person
-   Strict ethical guidelines and limited access policy

### Natural Language Processing

15-20%

#### Azure AI Language

-   Sentiment analysis: positive, negative, neutral, mixed
-   Key phrase extraction: main talking points
-   Named Entity Recognition (NER): people, places, organisations
-   PII detection: identify and redact personal info
-   Language detection: identify the language of text

#### Azure AI Speech

-   Speech-to-text (STT): transcription
-   Text-to-speech (TTS): synthesis with neural voices
-   Speech translation: real-time spoken language translation
-   Speaker recognition: identify who is speaking

#### Conversational AI

-   CLU: define intents (goals) and entities (details)
-   Question Answering: knowledge base from FAQs/docs
-   Azure Bot Service: build and deploy chatbots
-   Replaces LUIS and QnA Maker (now in Azure AI Language)

### Generative AI

15-20%

#### Azure OpenAI Service

-   GPT models: text and code generation
-   DALL-E: image generation from text prompts
-   Whisper: speech-to-text transcription
-   Enterprise security, compliance, and regional deployment

#### Prompt Engineering

-   System message: sets behaviour and constraints
-   Few-shot examples: show desired input/output pairs
-   Temperature: 0 = deterministic, 1+ = creative
-   Top-p: nucleus sampling, alternative to temperature

#### RAG & Grounding

-   RAG: retrieve relevant docs → inject into prompt → generate
-   Azure AI Search: vector + semantic search for retrieval
-   Grounding reduces hallucinations by anchoring to real data
-   On Your Data feature in Azure OpenAI for easy RAG setup

#### Microsoft Copilot

-   LLM + Microsoft Graph + grounding = Copilot
-   Microsoft 365 Copilot: Word, Excel, PowerPoint, Teams
-   Copilot Studio: build custom copilots (low-code)
-   Bing Chat (Copilot): web search + LLM answers

Section 6 / Method of Loci

## The Memory Palace

Walk through themed rooms — each object anchors a concept in spatial memory.

### The Principles Lobby

AI Workloads & Responsible AI — Where it all begins

🤖

AI Workloads

Prediction, anomaly detection, vision, NLP, conversational AI, generative AI

⚖️

Fairness

Treat all people equitably. Avoid bias in data and outcomes

🛡️

Reliability & Safety

AI must perform consistently and handle errors gracefully

🔒

Privacy & Security

Protect personal data. Secure models and endpoints

👁️

Transparency

Users should understand how AI makes decisions. Explain outputs

🤝

Accountability

Humans are responsible for AI outcomes — not the machine

### The Training Lab

Machine Learning — Where models learn

📈

Regression

Predict numbers. Linear regression. Metrics: MAE, RMSE, R²

🏷️

Classification

Predict categories. Logistic regression. Metrics: precision, recall, F1, AUC

🔵

Clustering

Find groups. K-Means. Unsupervised — no labels needed

⚙️

AutoML

Automated algorithm selection and hyperparameter tuning

🔧

Designer

Drag-and-drop pipeline builder. No code required

💻

Notebooks

Jupyter notebooks for code-first ML. Python and R supported

### The Vision Gallery

Computer Vision — Where machines see

🖼️

Image Analysis

Tags, captions, categories, objects. Azure AI Vision service

📄

OCR

Extract printed and handwritten text from images and PDFs

🎯

Custom Vision

Train classifiers and detectors with small datasets. Export to edge

👤

Face API

Detection (where), identification (who), verification (confirm match)

🔲

Object Detection

Bounding boxes with labels and confidence scores. Multiple objects per image

📷

Spatial Analysis

People counting, movement tracking, zone monitoring from video

### The Language Library

NLP & Speech — Where words have meaning

💬

Sentiment Analysis

Positive, negative, neutral, mixed. Scored per sentence and document

🔑

Entity Recognition

Identify people, places, organisations, dates, quantities in text

🎤

Speech Services

STT (transcribe), TTS (synthesise), translation, speaker recognition

🤖

CLU

Intents = what user wants. Entities = key details. Train with utterances

❓

Question Answering

Build knowledge bases from FAQs and documents. Returns best match

🌐

Translator

Real-time text translation across 100+ languages. Custom models available

### The Generation Chamber

Generative AI & Copilot — Where AI creates

✨

GPT Models

Text generation by predicting next token. Temperature controls randomness

🎨

DALL-E

Generate images from text prompts. Variations and edits supported

📝

Prompt Engineering

System message + few-shot examples + clear instructions = better output

🔍

RAG Pattern

Retrieve docs → inject into prompt → generate grounded answer. Reduces hallucinations

🤝

Microsoft Copilot

LLM + Microsoft Graph + grounding. Built into M365, Bing, Azure

🛡️

Content Safety

Violence, hate, sexual, self-harm filtering. Severity 0–6. On by default in Azure OpenAI

Section 7 / Pattern Recognition

## Pattern Spotter

Decision trees and trigger-answer pairs — see the pattern, know the answer.

Which ML Task Type?

Which ML Task Type?  
  ├── Predict a number (price, temperature) → Regression (supervised)  ├── Predict a category (yes/no, spam/legit) → Classification (supervised)  ├── Group similar items (no labels) → Clustering (unsupervised)  ├── Detect unusual patterns in data → Anomaly detection  ├── Generate new text, images, or code → Generative AI  ├── Understand or process human language → NLP workload  └── Analyse or interpret images/video → Computer vision workload

Which Azure AI Service?

Which Azure AI Service?  
  ├── Analyse images, generate captions, OCR → Azure AI Vision  ├── Train custom image classifier/detector → Custom Vision  ├── Detect/identify faces → Azure AI Face  ├── Sentiment, entities, key phrases → Azure AI Language  ├── Speech-to-text or text-to-speech → Azure AI Speech  ├── Translate text across languages → Azure AI Translator  ├── Generate text, code, or chat → Azure OpenAI Service (GPT)  └── Generate images from prompts → Azure OpenAI Service (DALL-E)

Which Responsible AI Principle?

Which Responsible AI Principle?  
  ├── Model disadvantages a specific group → Fairness  ├── System behaves unpredictably or unsafely → Reliability & Safety  ├── Personal data exposed or misused → Privacy & Security  ├── System excludes certain users → Inclusiveness  ├── Users can’t understand how AI decided → Transparency  └── No one owns the AI’s outcomes → Accountability

How to Build an ML Model in Azure?

How to Build an ML Model in Azure?  
  ├── Fastest path, no ML expertise → AutoML (automated algorithm selection)  ├── Visual pipeline, no code → Azure ML Designer (drag-and-drop)  ├── Full control, custom code → Azure ML Notebooks (Python/R)  ├── Common AI task (no custom model needed) → Azure AI Services (pre-built APIs)  └── Generative AI / LLM application → Azure OpenAI Service + AI Studio

## Decision Cards

“predict a numeric value” or “continuous output”→Regression

“predict a category” or “binary/multi-class”→Classification

“group similar items” or “no labelled data”→Clustering (K-Means)

“confusion matrix” or “TP/FP/TN/FN”→Classification evaluation

“R-squared” or “RMSE” or “MAE”→Regression evaluation metrics

“bounding boxes” on images→Object detection (Custom Vision)

“OCR” or “extract text from images”→Azure AI Vision (Read API)

“sentiment analysis” or “key phrases”→Azure AI Language

“intents and entities” for a chatbot→Conversational Language Understanding (CLU)

“speech-to-text” or “text-to-speech”→Azure AI Speech

“temperature” parameter for text generation→Controls LLM randomness (low = focused, high = creative)

“RAG” or “retrieval-augmented generation”→Search your data + inject into prompt + generate grounded answer

“prompt engineering” or “system message”→Optimise LLM output via instructions, examples, constraints

“Copilot” or “Microsoft 365 AI assistant”→LLM + Microsoft Graph + grounding

“content safety” or “harmful content detection”→Azure AI Content Safety (violence, hate, sexual, self-harm)

“treats all people equitably” or “bias in AI”→Fairness (Responsible AI principle)

“AutoML” or “automatic algorithm selection”→Azure Machine Learning AutoML

“drag-and-drop ML pipeline”→Azure Machine Learning Designer

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