The F1 score is the correct choice because it provides a much more informative evaluation for imbalanced datasets than accuracy does. When 90% of your data is the majority class, a model that simply predicts that class every time will achieve 90% accuracy, masking its complete failure to identify the minority class. The F1 score, as the harmonic mean of precision and recall, directly penalizes both false positives and false negatives, making it the standard metric for assessing performance on the rare positive class. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of why accuracy is a trap in imbalanced classification scenarios—a common trick question presents high accuracy as a sign of success. To remember this, think of the F1 score as the "balance beam" for rare events: it forces the model to be both precise and sensitive, not just blindly accurate.
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.
A data scientist is training a model to predict whether a customer will purchase a product (Yes/No). The dataset contains 90% 'No' and 10% 'Yes'. After training, the model achieves 90% accuracy. Which evaluation metric would be more informative to assess the model's performance on the minority class?
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 this imbalanced dataset (90% 'No', 10% 'Yes'), a model that always predicts 'No' would achieve 90% accuracy, making accuracy a misleading metric. The F1 score is the harmonic mean of precision and recall, specifically designed to evaluate a model's performance on the minority class by balancing false positives and false negatives. It is the most informative metric here because it directly measures how well the model identifies the rare 'Yes' purchases without being inflated by the majority class.
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.
✗
Mean Absolute Error (MAE)
Why it's wrong here
MAE is used for regression tasks to measure average error magnitude, not for classification with imbalanced classes.
✓
F1 score
Why this is correct
Correct. F1 score combines precision and recall, making it ideal for evaluating performance on the minority class in imbalanced datasets.
Related concept
Read the scenario before looking for a memorised answer.
✗
Area Under the Curve (AUC)
Why it's wrong here
AUC measures the model's ability to rank predictions, but F1 is more directly interpretable for the minority class performance.
✗
R-squared
Why it's wrong here
R-squared is a regression metric that indicates the proportion of variance explained; not applicable to binary classification.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates see 90% accuracy and assume the model is performing well, failing to recognize that accuracy is misleading in imbalanced datasets, and they may incorrectly select AUC because it is a common classification metric, but it does not directly penalize poor minority-class performance like the F1 score does.
Detailed technical explanation
How to think about this question
The F1 score is computed as 2 * (precision * recall) / (precision + recall), where precision = TP/(TP+FP) and recall = TP/(TP+FN). In a 90/10 imbalance, a naive model that predicts 'No' for all samples yields precision=0 and recall=0 for the minority class, resulting in an F1 score of 0, which correctly flags poor performance. In contrast, accuracy would be 90%, hiding the model's failure to detect any 'Yes' purchases. This makes F1 essential for scenarios like fraud detection or rare disease diagnosis, where missing the minority class has high cost.
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 this imbalanced dataset (90% 'No', 10% 'Yes'), a model that always predicts 'No' would achieve 90% accuracy, making accuracy a misleading metric. The F1 score is the harmonic mean of precision and recall, specifically designed to evaluate a model's performance on the minority class by balancing false positives and false negatives. It is the most informative metric here because it directly measures how well the model identifies the rare 'Yes' purchases without being inflated by the majority class.
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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