The correct answer is an autonomous system using an LLM to plan and execute multi-step tasks using tools. This is correct because an AI agent in Azure AI and generative AI contexts leverages a large language model to reason through complex objectives, break them into sequential steps, and call external APIs or tools—such as Azure AI Search or custom connectors—to complete workflows without requiring constant human input. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of agentic AI within the Azure AI Agent Service, often appearing as a scenario where an LLM must retrieve data or trigger actions autonomously. A common trap is confusing a simple chatbot with an agent; remember that an agent actively plans and acts, not just responds. Memory tip: think “Plan, Tool, Act” to recall the agent’s core cycle of reasoning, tool use, and execution.
AI-900 Practice Question: Describe features of generative AI workloads on Azure
This AI-900 practice question tests your understanding of describe features of generative ai workloads on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
What is an AI agent in the context of Azure AI and generative AI?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
An autonomous system using an LLM to plan and execute multi-step tasks using tools
Option B is correct because an AI agent in Azure AI and generative AI contexts refers to an autonomous system that leverages a large language model (LLM) to reason, plan, and execute multi-step tasks by calling external tools or APIs. This aligns with the Azure AI Agent Service, which enables agents to orchestrate workflows, retrieve information, and perform actions without continuous human intervention, embodying the core concept of agentic AI.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
A human employee who manages AI model deployments
Why it's wrong here
Human employees are people — AI agents are autonomous software systems using LLMs to reason and act.
✓
An autonomous system using an LLM to plan and execute multi-step tasks using tools
Why this is correct
AI agents use LLMs to reason about goals, plan steps, use tools (search, APIs), and execute actions autonomously.
Related concept
Read the scenario before looking for a memorised answer.
✗
A monitoring agent that checks AI model health automatically
Why it's wrong here
Health monitoring is operations tooling — AI agents are task-completion systems that use LLMs to reason and act.
✗
A software robot that scrapes websites for training data
Why it's wrong here
Web scrapers are data collection tools — AI agents use LLMs to reason and take actions toward goals.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse the general term 'agent' (e.g., monitoring agents or human agents) with the specific generative AI concept of an LLM-powered autonomous task executor, leading them to pick options like C or A.
Detailed technical explanation
How to think about this question
Under the hood, an AI agent in Azure AI uses an LLM as its reasoning engine, combined with a planner (e.g., ReAct or function-calling) to decompose a user request into steps, then invokes tools like Azure Functions, Bing Search, or custom APIs via the Azure AI Agent Service SDK. A real-world scenario is a customer support agent that retrieves order status from a database, checks inventory via an API, and drafts a response—all orchestrated by the LLM without hardcoded logic.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Describe features of generative AI workloads on Azure — This question tests Describe features of generative AI workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: An autonomous system using an LLM to plan and execute multi-step tasks using tools — Option B is correct because an AI agent in Azure AI and generative AI contexts refers to an autonomous system that leverages a large language model (LLM) to reason, plan, and execute multi-step tasks by calling external tools or APIs. This aligns with the Azure AI Agent Service, which enables agents to orchestrate workflows, retrieve information, and perform actions without continuous human intervention, embodying the core concept of agentic AI.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. What is 'agentic AI' and how does it differ from a simple chatbot?
hard
A.AI that represents a company as a legal agent for contractual purposes
✓ B.AI that autonomously plans and executes multi-step workflows using tools to accomplish complex goals
C.Chatbots that can respond on behalf of a company's customer service team
D.AI models that were trained by multiple agents working simultaneously in parallel
Why B: Agentic AI refers to AI systems that can autonomously plan and execute multi-step workflows by using external tools, APIs, or data sources to achieve complex goals. This differs from a simple chatbot, which typically responds to user prompts in a single turn without independent goal-setting or tool orchestration. In generative AI workloads on Azure, agentic AI might leverage Azure AI Agent Service or Semantic Kernel to chain together calls to Azure Cognitive Search, Azure Functions, or external APIs, enabling tasks like automated report generation or multi-step data analysis.
Last reviewed: Jun 11, 2026
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