AI-900 Exam Flashcards

Q: What do AI-900 flashcards help you review?

These AI-900 flashcards review Azure AI workloads, responsible AI principles, machine learning concepts, computer vision, natural language processing, speech, and generative AI services.

Q: Are AI-900 flashcards enough to pass the exam?

Flashcards are useful for memorizing terms and service differences, but candidates should also practice scenario questions and review Microsoft's official AI-900 skills outline.

Q: How should I use these AI-900 flashcards?

Use the flashcards for quick recall, mark weak topics such as responsible AI or Azure AI services, then follow up with practice questions and a study guide before exam day.

Question 1

Machine learning

Accepted Answer

Machine learning lets systems learn patterns from data instead of relying only on explicit rules. It matters because many AI solutions improve by training on examples.

Question 2

Prediction workload

Accepted Answer

Prediction uses historical data to estimate future values or outcomes. Common examples include sales forecasts, demand planning, and churn risk.

Question 3

Anomaly detection

Accepted Answer

Anomaly detection identifies unusual patterns or outliers. It is used for fraud detection, equipment monitoring, and security alerts.

Question 4

Computer vision

Accepted Answer

Computer vision analyzes images and video. It supports tasks such as image classification, object detection, OCR, and spatial analysis.

Question 5

Natural language processing

Accepted Answer

Natural language processing helps systems understand, analyze, or generate human language. It powers sentiment analysis, translation, summarization, and chat experiences.

Question 6

Conversational AI

Accepted Answer

Conversational AI uses bots or agents to interact with users through natural language. It matters for support, self-service, and guided workflows.

Question 7

Knowledge mining

Accepted Answer

Knowledge mining extracts searchable information from large content collections. Azure AI Search is commonly used to index documents and enrich them with AI skills.

Question 8

Fairness

Accepted Answer

Fairness means AI systems should avoid unjust bias and treat people equitably. It matters because biased data or design can create harmful outcomes.

Question 9

Reliability and safety

Accepted Answer

Reliability and safety means AI systems should work consistently and fail safely. Testing, monitoring, and fallback plans reduce risk in real use.

Question 10

Privacy and security

Accepted Answer

Privacy and security protect data used by AI systems and prevent unauthorized access. This matters because AI workloads often process sensitive information.

Question 11

Inclusiveness

Accepted Answer

Inclusiveness means AI should work for people with different abilities, languages, and contexts. It encourages accessible and broadly usable solutions.

Question 12

Transparency

Accepted Answer

Transparency means users should understand what an AI system does and how decisions are influenced. It supports trust and appropriate human review.

Question 13

Accountability

Accepted Answer

Accountability means people and organizations remain responsible for AI outcomes. Governance, documentation, and review processes help enforce responsibility.

Question 14

Supervised learning

Accepted Answer

Supervised learning trains a model with labeled examples. It is used when the expected output, such as a category or numeric value, is known during training.

Question 15

Unsupervised learning

Accepted Answer

Unsupervised learning finds patterns in unlabeled data. Clustering similar customers or documents is a common exam example.

Question 16

Classification

Accepted Answer

Classification predicts a category or class label. Examples include spam detection, disease risk category, and sentiment labels.

Question 17

Regression

Accepted Answer

Regression predicts a numeric value. Examples include price, demand, temperature, or delivery time estimates.

Question 18

Clustering

Accepted Answer

Clustering groups similar items without predefined labels. It helps discover natural segments in customers, products, or documents.

Question 19

Training dataset

Accepted Answer

A training dataset is used to teach a model patterns. Good training data should represent the problem and avoid avoidable bias.

Question 20

Validation dataset

Accepted Answer

A validation dataset helps tune and compare models during development. It checks performance on data not directly used for training.

Question 21

Test dataset

Accepted Answer

A test dataset evaluates final model performance on held-out data. It matters because high training performance alone can hide overfitting.

Question 22

Azure Machine Learning

Accepted Answer

Azure Machine Learning is a cloud platform for building, training, deploying, and managing machine learning models. It supports code-first and designer workflows.

Question 23

Image classification

Accepted Answer

Image classification assigns one or more labels to an image. It answers questions such as whether an image contains a product, defect, or scene type.

Question 24

Object detection

Accepted Answer

Object detection identifies objects and their locations in an image. It returns bounding boxes, not just image-level labels.

Question 25

OCR

Accepted Answer

Optical character recognition extracts printed or handwritten text from images and documents. It matters for forms, receipts, IDs, and scanned files.

Question 26

Face detection

Accepted Answer

Face detection locates human faces in images. It differs from face recognition, which tries to identify or verify a person.

Question 27

Azure AI Vision

Accepted Answer

Azure AI Vision provides image analysis, OCR, and related visual AI capabilities through managed APIs. It reduces the need to build custom models from scratch.

Question 28

Azure AI Document Intelligence

Accepted Answer

Document Intelligence extracts fields, tables, and text from forms and documents. It is used when structured information must be pulled from PDFs or images.

Question 29

Sentiment analysis

Accepted Answer

Sentiment analysis estimates whether text expresses positive, negative, neutral, or mixed opinion. It is common in customer feedback analysis.

Question 30

Key phrase extraction

Accepted Answer

Key phrase extraction identifies important words or phrases in text. It helps summarize documents and tag content for search or review.

Question 31

Named entity recognition

Accepted Answer

Named entity recognition detects entities such as people, places, organizations, dates, and quantities. It helps structure unstructured text.

Question 32

Language detection

Accepted Answer

Language detection identifies the language of text. It is useful before translation, routing, or applying language-specific analysis.

Question 33

Translation

Accepted Answer

Translation converts text from one language to another. Azure AI Translator supports multilingual applications without custom language models.

Question 34

Speech to text

Accepted Answer

Speech to text converts spoken audio into written text. It supports transcription, captions, and voice-driven applications.

Question 35

Text to speech

Accepted Answer

Text to speech converts written text into synthetic speech. It is used for accessibility, voice assistants, and audio responses.

Question 36

Speech translation

Accepted Answer

Speech translation translates spoken input across languages. It combines speech recognition, translation, and sometimes speech synthesis.

Question 37

Azure AI Language

Accepted Answer

Azure AI Language provides text analytics and language understanding features. It supports sentiment, key phrases, entities, question answering, and custom text tasks.

Question 38

Question answering

Accepted Answer

Question answering returns answers from a knowledge base or source content. It is often used for FAQ bots and support assistants.

Question 39

Generative AI

Accepted Answer

Generative AI creates new content such as text, code, images, or summaries from learned patterns. It differs from AI that only classifies or predicts.

Question 40

Large language model

Accepted Answer

A large language model predicts and generates text based on patterns learned from large text datasets. It powers chat, summarization, and content generation.

Question 41

Prompt

Accepted Answer

A prompt is the input or instruction given to a generative AI model. Clear prompts improve relevance, formatting, and task completion.

Question 42

System message

Accepted Answer

A system message gives high-level instructions that guide model behavior. It is useful for setting role, tone, constraints, and safety expectations.

Question 43

Grounding

Accepted Answer

Grounding connects model responses to supplied data or trusted sources. It reduces unsupported answers and helps responses fit the organization's content.

Question 44

RAG

Accepted Answer

Retrieval augmented generation retrieves relevant content and provides it to a model before generation. It helps answer with current or private knowledge without retraining the model.

Question 45

Azure OpenAI

Accepted Answer

Azure OpenAI provides access to advanced generative AI models through Azure governance, security, and integration features. It is central to many Azure generative AI scenarios.

Question 46

Copilot

Accepted Answer

A copilot is an AI assistant embedded in a product or workflow. It helps users complete tasks through natural language and contextual suggestions.

Question 47

Content filters

Accepted Answer

Content filters help detect and reduce harmful or inappropriate generated content. They support responsible use of generative AI systems.

Question 48

Token

Accepted Answer

A token is a chunk of text processed by a language model. Token limits affect prompt size, response length, and cost.

Question 49

Model deployment

Accepted Answer

A model deployment makes a selected model available through an endpoint. In Azure AI services, deployment configuration controls how applications call the model.

Question 50

Responsible generative AI

Accepted Answer

Responsible generative AI includes grounding, human review, content filtering, transparency, and privacy controls. These practices reduce risk when models produce open-ended content.

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