Question 828 of 1,020

Describe fundamental principles of machine learning on Azure →mediumMultiple ChoiceObjective-mapped

What is a Confusion Matrix in Machine Learning?

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.

What is 'confusion matrix' and what does it tell you about a classification model?

A
A measure of how confused users are when interacting with an AI system's predictions
Why wrong: User confusion is a UX metric — the confusion matrix is a technical evaluation table showing prediction error types.
B
A table showing counts of correct and incorrect predictions broken down by predicted vs. actual class
The confusion matrix shows TP, TN, FP, FN counts — enabling calculation of precision, recall, F1, and understanding error types.
C
A graphical display of how confident the model is across its entire test dataset
Why wrong: Confidence distributions are probability plots — the confusion matrix shows actual vs. predicted class counts.
D
A diagram comparing the accuracy of multiple models on the same test set
Why wrong: Multi-model comparison is an evaluation technique — the confusion matrix is a per-model analysis of one model's prediction errors.

Full breakdown with real-world context →

Quick Answer

The correct answer is a table showing counts of correct and incorrect predictions broken down by predicted versus actual class. This is because a confusion matrix provides a detailed breakdown of a classification model’s performance by tallying true positives, true negatives, false positives, and false negatives for each class, allowing you to see not just overall accuracy but exactly where the model is making mistakes. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your ability to evaluate classifiers in Azure Machine Learning, often appearing in questions about model performance metrics or comparing algorithms. A common trap is confusing a confusion matrix with a simple accuracy score—remember, the matrix reveals error types, not just a single number. For a quick memory tip, think of the four quadrants: the diagonal tells you what the model got right, while the off-diagonal cells show the specific misclassifications.

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

✓

A table showing counts of correct and incorrect predictions broken down by predicted vs. actual class

Option B is correct because a confusion matrix is a specific table layout that allows visualization of the performance of a classification model. It shows the counts of true positive, true negative, false positive, and false negative predictions, broken down by each actual class versus each predicted class. This directly tells you not just overall accuracy, but also the types of errors the model is making, which is critical for evaluating classifiers in Azure Machine 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.

✗
A measure of how confused users are when interacting with an AI system's predictions
Why it's wrong here
User confusion is a UX metric — the confusion matrix is a technical evaluation table showing prediction error types.
✓
A table showing counts of correct and incorrect predictions broken down by predicted vs. actual class
Why this is correct
The confusion matrix shows TP, TN, FP, FN counts — enabling calculation of precision, recall, F1, and understanding error types.
Related concept
Read the scenario before looking for a memorised answer.
✗
A graphical display of how confident the model is across its entire test dataset
Why it's wrong here
Confidence distributions are probability plots — the confusion matrix shows actual vs. predicted class counts.
✗
A diagram comparing the accuracy of multiple models on the same test set
Why it's wrong here
Multi-model comparison is an evaluation technique — the confusion matrix is a per-model analysis of one model's prediction errors.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates confuse the term 'confusion matrix' with user confusion or model confidence, when in fact it is a structured table of prediction counts that reveals the specific types of correct and incorrect classifications.

Trap categories for this question

Similar concept trap
User confusion is a UX metric — the confusion matrix is a technical evaluation table showing prediction error types.
Command / output trap
User confusion is a UX metric — the confusion matrix is a technical evaluation table showing prediction error types.

Detailed technical explanation

How to think about this question

Under the hood, a confusion matrix is a cross-tabulation of predicted class labels (columns) versus true class labels (rows). For binary classification, the four cells are TP, FP, FN, and TN, from which derived metrics like precision (TP/(TP+FP)), recall (TP/(TP+FN)), and F1-score are computed. In Azure Machine Learning, the confusion matrix is automatically generated in the Evaluate Model module and is essential for understanding class imbalance—for example, in fraud detection, a model with 99% accuracy might still miss all fraudulent transactions, which the confusion matrix would reveal via a high false negative count.

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.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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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: A table showing counts of correct and incorrect predictions broken down by predicted vs. actual class — Option B is correct because a confusion matrix is a specific table layout that allows visualization of the performance of a classification model. It shows the counts of true positive, true negative, false positive, and false negative predictions, broken down by each actual class versus each predicted class. This directly tells you not just overall accuracy, but also the types of errors the model is making, which is critical for evaluating classifiers in Azure Machine 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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Same concept, more angles

1 more ways this is tested on AI-900

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 does 'model accuracy' measure in machine learning classification?

medium

A.How quickly the model makes predictions
✓ B.The proportion of correct predictions out of total predictions
C.How much memory the model uses during inference
D.The number of training examples used to build the model

Why B: Model accuracy in classification measures the ratio of correctly predicted instances to the total number of predictions made. It is calculated as (True Positives + True Negatives) / (Total Predictions). This metric is fundamental in evaluating classification models on Azure Machine Learning, where it is reported in the model evaluation metrics.

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