AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
This AI-900 practice question tests your understanding of describe fundamental principles of machine learning 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.
A robotics team is training a robot to navigate a maze. The robot receives a positive reward (+10) when it reaches the exit and a negative reward (-1) every time it bumps into a wall. The robot learns to maximize its cumulative reward over multiple trials. Which type of machine learning is being used?
The robot receives rewards based on its actions and learns to maximize them, which is the core principle of reinforcement learning.
B
Supervised learning
Why wrong: Supervised learning uses labeled input-output pairs, not a reward signal.
C
Unsupervised learning
Why wrong: Unsupervised learning finds hidden structures in data without feedback from rewards.
D
Semi-supervised learning
Why wrong: Semi-supervised learning uses a small amount of labeled data and a large amount of unlabeled data, not a reward signal from the environment.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Reinforcement learning
The robot learns by interacting with its environment, receiving rewards (positive for reaching the exit, negative for bumping into walls), and adjusting its behavior to maximize cumulative reward over time. This trial-and-error learning process, where an agent learns a policy through feedback from its actions, is the defining characteristic of reinforcement learning.
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.
✓
Reinforcement learning
Why this is correct
The robot receives rewards based on its actions and learns to maximize them, which is the core principle of reinforcement learning.
Related concept
Read the scenario before looking for a memorised answer.
✗
Supervised learning
Why it's wrong here
Supervised learning uses labeled input-output pairs, not a reward signal.
✗
Unsupervised learning
Why it's wrong here
Unsupervised learning finds hidden structures in data without feedback from rewards.
✗
Semi-supervised learning
Why it's wrong here
Semi-supervised learning uses a small amount of labeled data and a large amount of unlabeled data, not a reward signal from the environment.
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 because both involve 'learning from feedback,' but they fail to recognize that reinforcement learning uses evaluative feedback (rewards) rather than instructive feedback (labeled examples).
Trap categories for this question
Command / output trap
Supervised learning uses labeled input-output pairs, not a reward signal.
Detailed technical explanation
How to think about this question
Reinforcement learning (RL) is formalized using Markov Decision Processes (MDPs), where the agent's goal is to learn an optimal policy π* that maximizes the expected discounted cumulative reward (return). In this maze scenario, the robot's actions (move, turn) and states (position, orientation) define the MDP, and algorithms like Q-learning or Deep Q-Networks (DQN) can be used to approximate the optimal action-value function. A subtle behavior is the exploration-exploitation trade-off: the robot must sometimes bump into walls (explore) to discover better paths, rather than always taking the currently known best action (exploit).
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 fundamental principles of machine learning on Azure — This question tests Describe fundamental principles of machine learning on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Reinforcement learning — The robot learns by interacting with its environment, receiving rewards (positive for reaching the exit, negative for bumping into walls), and adjusting its behavior to maximize cumulative reward over time. This trial-and-error learning process, where an agent learns a policy through feedback from its actions, is the defining characteristic of reinforcement learning.
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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Question Discussion
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