AI-900 Practice Question: Describe features of generative AI workloads on Azure
What is generative AI?
⚠ Common exam trap
Watch out — candidates often confuse generative AI with discriminative AI tasks (like classification or anomaly detection) because both involve learning from data, but generative AI's defining characteristic is the creation of new content, not just analysis or labeling.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
AI that creates new content such as text, images, or code based on learned patterns
Generative AI refers to models that learn patterns from training data and then produce new, original content—such as text, images, audio, or code—that resembles the training distribution. Unlike discriminative models that map inputs to labels, generative models (e.g., GPT, DALL-E) sample from a learned probability distribution to create novel outputs. This is the core definition tested in AI-900 for the 'features of generative AI workloads' domain.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AI that classifies existing data into predefined categories
Why it's wrong here
Classification assigns each input to a predefined category by learning a decision boundary (for example, P(label|input)), so the output is always a discrete class label from a fixed set. This is a discriminative task. Generative AI models the underlying probability distribution of the data itself (P(input) or P(input,label)) and can generate new examples; therefore, classifying existing data into categories is the opposite of generative content creation.
- ✓
AI that creates new content such as text, images, or code based on learned patterns
Why this is correct
Generative AI is defined by its output: new, original content that is statistically consistent with the patterns learned during training. Models such as GPT-4, DALL-E, and GitHub Copilot learn the probability distribution over text, images, or source code and then sample from that distribution to generate coherent, contextually relevant outputs. The key is that the model creates artifacts that did not previously exist, rather than merely recognizing or categorizing existing inputs.
- ✗
AI that detects anomalies in structured data
Why it's wrong here
Anomaly detection in structured data is a scoring/labeling task: each record is examined and assigned an anomaly score or a normal/anomalous label based on deviation from expected patterns. It is a form of one-class discrimination or density estimation used for detection, but its output is a decision about existing data, not newly synthesized content. Generative AI would instead learn to produce new data rows that resemble the training distribution, which serves a fundamentally different purpose.
- ✗
AI that controls physical robots
Why it's wrong here
Robotic control is fundamentally a reactive/closed-loop process: sensor data is fed into control algorithms or learned policies to output motor commands. Even when deep learning is used, the objective is to select an action that achieves a goal, not to produce original artifacts. Generative AI, by contrast, models the statistical distribution of training data and samples from that distribution to create new text, images, or code.
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Related to this question
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Types of AI Workloads
Key term
Generative AI
Generative AI is a type of artificial intelligence that creates new content—like text, images, or code—by learning patterns from existing data.
Key term
Azure OpenAI Service
Azure OpenAI Service is a cloud platform from Microsoft that lets developers use powerful artificial intelligence models, like GPT-4, to build applications that can understand and generate human-like text, code, images, and more.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.