The answer is the F1 score, because it provides a much more reliable evaluation metric for imbalanced classification than accuracy. In a dataset where only 5% of cases represent churn, a model that simply predicts “not churn” for every customer will achieve 95% accuracy, but this is completely misleading—it fails to identify any actual churn cases. The F1 score combines precision and recall into a single harmonic mean, penalizing both false positives and false negatives, so it is not skewed by the overwhelming majority class. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how to select appropriate evaluation metrics for real-world data imbalances, a common trap where candidates mistakenly choose accuracy. A helpful memory tip: think of F1 as the “balance score”—it only looks good when your model actually catches the rare, important cases.
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. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 data scientist is building a classification model to predict customer churn. The dataset has only 5% churn cases. The model achieves 95% accuracy on the test set, but upon investigation, the data scientist finds the model predicts 'not churn' for nearly every customer. Which metric should the data scientist primarily use to evaluate the model's performance on this imbalanced dataset?
Why wrong: Accuracy alone is misleading because the model can achieve 95% by simply predicting 'not churn' for all cases, ignoring the minority class entirely.
B
F1 score
The F1 score is the harmonic mean of precision and recall, making it a robust metric for imbalanced datasets as it accounts for false positives and false negatives.
C
Mean Absolute Error (MAE)
Why wrong: MAE is used for regression tasks, not for binary classification problems like churn prediction.
D
R-squared
Why wrong: R-squared is a metric for regression models that indicates the proportion of variance explained; it is not applicable to classification tasks.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
F1 score
In an imbalanced dataset with only 5% churn, a model that predicts 'not churn' for every case achieves 95% accuracy by always guessing the majority class. This accuracy is misleading because it fails to identify any churn cases. The F1 score (option B) is the harmonic mean of precision and recall, making it the primary metric for evaluating classification performance on imbalanced data, as it penalizes both false positives and false negatives and is not skewed by class imbalance.
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.
✗
Accuracy
Why it's wrong here
Accuracy alone is misleading because the model can achieve 95% by simply predicting 'not churn' for all cases, ignoring the minority class entirely.
✓
F1 score
Why this is correct
The F1 score is the harmonic mean of precision and recall, making it a robust metric for imbalanced datasets as it accounts for false positives and false negatives.
Related concept
Read the scenario before looking for a memorised answer.
✗
Mean Absolute Error (MAE)
Why it's wrong here
MAE is used for regression tasks, not for binary classification problems like churn prediction.
✗
R-squared
Why it's wrong here
R-squared is a metric for regression models that indicates the proportion of variance explained; it is not applicable to classification tasks.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often default to accuracy as the primary metric for classification, failing to recognize that on imbalanced datasets, accuracy can be artificially high and misleading, while the F1 score provides a more truthful evaluation of minority class prediction.
Detailed technical explanation
How to think about this question
The F1 score combines precision (true positives / (true positives + false positives)) and recall (true positives / (true positives + false negatives)) into a single metric that balances both, making it robust for imbalanced datasets. In Azure Machine Learning, the F1 score is automatically computed for classification models and is often used alongside precision-recall curves to evaluate model performance. A real-world scenario is fraud detection, where fraudulent transactions are rare (e.g., 1%), and a model with high accuracy but low F1 score would miss most fraud cases, leading to significant financial losses.
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: F1 score — In an imbalanced dataset with only 5% churn, a model that predicts 'not churn' for every case achieves 95% accuracy by always guessing the majority class. This accuracy is misleading because it fails to identify any churn cases. The F1 score (option B) is the harmonic mean of precision and recall, making it the primary metric for evaluating classification performance on imbalanced data, as it penalizes both false positives and false negatives and is not skewed by class imbalance.
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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