- A
A category of input features used by the model
Why wrong: Input categories are features — labels are the desired output values the model learns to predict.
- B
The correct output or answer associated with each training example that the model learns to predict
Labels are the ground truth answers in training data — the model learns to produce predictions that match the labels.
- C
A text description attached to a model explaining what it does
Why wrong: Model descriptions are documentation — labels are the target outputs in training data.
- D
A tag applied to Azure ML resources for organization
Why wrong: Azure resource tags are organizational metadata — ML labels are the correct answer values in training data.
Question 287 of 1,020
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. 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 is the role of a label (also called target or ground truth) in supervised machine learning?
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
✓
The correct output or answer associated with each training example that the model learns to predict
In supervised machine learning, the label (also called target or ground truth) is the known correct output for each training example. The model uses these labels during training to learn the mapping from input features to outputs, enabling it to make accurate predictions on new, unseen data. This is fundamental to supervised learning, where the algorithm minimizes the error between its predictions and the ground truth labels.
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 category of input features used by the model
Why it's wrong here
Input categories are features — labels are the desired output values the model learns to predict.
- ✓
The correct output or answer associated with each training example that the model learns to predict
Why this is correct
Labels are the ground truth answers in training data — the model learns to produce predictions that match the labels.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
A text description attached to a model explaining what it does
Why it's wrong here
Model descriptions are documentation — labels are the target outputs in training data.
- ✗
A tag applied to Azure ML resources for organization
Why it's wrong here
Azure resource tags are organizational metadata — ML labels are the correct answer values in training data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing the term 'label' in machine learning (ground truth output) with the general concept of a 'label' as a tag or category, leading candidates to mistakenly choose Option A or D.
Trap categories for this question
Command / output trap
Input categories are features — labels are the desired output values the model learns to predict.
Detailed technical explanation
How to think about this question
Under the hood, labels are used to compute the loss function (e.g., cross-entropy for classification, mean squared error for regression), which quantifies the difference between predicted and actual values. During backpropagation, gradients are calculated based on this loss to update model weights. In a real-world scenario like fraud detection, labels (e.g., 'fraudulent' or 'legitimate') must be accurate and representative; mislabeled data can lead to biased models and poor generalization.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
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: The correct output or answer associated with each training example that the model learns to predict — In supervised machine learning, the label (also called target or ground truth) is the known correct output for each training example. The model uses these labels during training to learn the mapping from input features to outputs, enabling it to make accurate predictions on new, unseen data. This is fundamental to supervised learning, where the algorithm minimizes the error between its predictions and the ground truth labels.
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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Last reviewed: Jun 11, 2026
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.
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