Question 598 of 1,020

Describe Artificial Intelligence workloads and considerations →easyMultiple ChoiceObjective-mapped

What Type of AI Workload Trains a Model to Play Games by Rewarding Successful Moves?

This AI-900 practice question tests your understanding of describe artificial intelligence workloads and considerations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 type of AI workload involves training a model to play games by rewarding successful moves?

A
Supervised learning with labeled game states
Why wrong: Labeling all game states would require expert annotations — reinforcement learning learns from rewards in the game environment itself.
B
Reinforcement learning where the agent receives rewards for successful moves
Game-playing AI uses reinforcement learning — rewards for winning moves and penalties for losing moves train the agent's strategy.
C
Clustering similar game strategies together
Why wrong: Clustering groups similar data — reinforcement learning trains agents through environmental reward signals.
D
Regression to predict the final game score
Why wrong: Score prediction is a specific ML task — full game-playing ability requires reinforcement learning.

Full breakdown with real-world context →

Quick Answer

The answer is reinforcement learning, the AI workload that trains a model to play games by rewarding successful moves. This is correct because reinforcement learning relies on an agent that interacts with an environment—like a game board or simulation—and learns optimal behavior through a system of rewards and penalties; each successful move increases the cumulative reward, guiding the agent toward better long-term strategies through trial and error. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your ability to distinguish reinforcement learning from supervised or unsupervised workloads, often appearing in scenarios involving game-playing, robotics, or resource optimization. A common trap is confusing it with supervised learning, but remember: reinforcement learning has no labeled dataset—the agent discovers correct actions on its own. For a quick memory tip, think of a dog learning a trick: every time it sits on command, it gets a treat—that’s reinforcement learning in action, with rewards shaping behavior over time.

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

✓

Reinforcement learning where the agent receives rewards for successful moves

Reinforcement learning is the correct AI workload because it involves an agent learning to make decisions by interacting with an environment and receiving rewards or penalties for its actions. In game-playing scenarios, the model is trained through trial and error, where successful moves are rewarded, guiding the agent to maximize cumulative reward over time.

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.

✗
Supervised learning with labeled game states
Why it's wrong here
Labeling all game states would require expert annotations — reinforcement learning learns from rewards in the game environment itself.
✓
Reinforcement learning where the agent receives rewards for successful moves
Why this is correct
Game-playing AI uses reinforcement learning — rewards for winning moves and penalties for losing moves train the agent's strategy.
Related concept
Read the scenario before looking for a memorised answer.
✗
Clustering similar game strategies together
Why it's wrong here
Clustering groups similar data — reinforcement learning trains agents through environmental reward signals.
✗
Regression to predict the final game score
Why it's wrong here
Score prediction is a specific ML task — full game-playing ability requires reinforcement learning.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates may confuse reinforcement learning with supervised learning, thinking that the model is trained on labeled game states, when in fact the agent learns from rewards without explicit correct answers.

Trap categories for this question

Similar concept trap
Clustering groups similar data — reinforcement learning trains agents through environmental reward signals.

Detailed technical explanation

How to think about this question

Reinforcement learning uses a Markov Decision Process (MDP) framework, where the agent selects actions based on a policy, and the environment returns a reward and next state. Algorithms like Q-learning or Deep Q-Networks (DQN) update action-value functions to approximate optimal behavior, often using experience replay and target networks to stabilize training. In practice, this approach powers systems like AlphaGo and game-playing bots that learn complex strategies from scratch.

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.

What to study next

Got this wrong? Here's your next step.

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FAQ

Questions learners often ask

What does this AI-900 question test?

Describe Artificial Intelligence workloads and considerations — This question tests Describe Artificial Intelligence workloads and considerations — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Reinforcement learning where the agent receives rewards for successful moves — Reinforcement learning is the correct AI workload because it involves an agent learning to make decisions by interacting with an environment and receiving rewards or penalties for its actions. In game-playing scenarios, the model is trained through trial and error, where successful moves are rewarded, guiding the agent to maximize cumulative reward over time.

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.

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