- A
Precision
Why wrong: Precision indicates the fraction of correct predictions among all predictions for that class, but it does not consider false negatives. While useful, F1-score combines precision and recall for a more complete picture.
- B
Recall
Why wrong: Recall indicates the fraction of actual instances of that class that were correctly predicted, but it ignores false positives. F1-score is preferred for a balanced view.
- C
F1-score
F1-score is the harmonic mean of precision and recall. It provides a single metric that balances both for a specific class, making it ideal for evaluating performance on the underperforming Iris virginica class.
- D
Mean Absolute Error (MAE)
Why wrong: MAE is a metric used for regression models, not classification. It measures the average magnitude of errors in continuous predictions, and is not applicable to flower species classification.
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. A key principle to apply: f1-score is the harmonic mean of precision and recall.. 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 trains a multiclass classification model to identify different species of flowers (Iris setosa, Iris virginica, Iris versicolor). The overall accuracy is 94%, but the accuracy for the Iris virginica class is only 60%. Which additional metric should the data scientist examine to better understand the model's performance on the minority class?
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
F1-score
The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both false positives and false negatives. Since the model has high overall accuracy but poor performance on the minority class (Iris virginica), the F1-score is ideal for evaluating the model's effectiveness on that class, as it accounts for class imbalance better than accuracy alone.
Key principle: F1-score is the harmonic mean of precision and recall.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Precision
Why it's wrong here
Precision indicates the fraction of correct predictions among all predictions for that class, but it does not consider false negatives. While useful, F1-score combines precision and recall for a more complete picture.
- ✗
Recall
Why it's wrong here
Recall indicates the fraction of actual instances of that class that were correctly predicted, but it ignores false positives. F1-score is preferred for a balanced view.
- ✓
F1-score
Why this is correct
F1-score is the harmonic mean of precision and recall. It provides a single metric that balances both for a specific class, making it ideal for evaluating performance on the underperforming Iris virginica class.
Related concept
F1-score is the harmonic mean of precision and recall.
- ✗
Mean Absolute Error (MAE)
Why it's wrong here
MAE is a metric used for regression models, not classification. It measures the average magnitude of errors in continuous predictions, and is not applicable to flower species classification.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose precision or recall individually, not realizing that the F1-score is specifically designed to combine both metrics and is the standard choice for evaluating performance on imbalanced classes in classification tasks.
Detailed technical explanation
How to think about this question
The F1-score is calculated as 2 * (precision * recall) / (precision + recall), and it ranges from 0 to 1, where 1 indicates perfect precision and recall. In multiclass classification, the F1-score can be computed per class (e.g., using 'macro' or 'weighted' averaging) to isolate performance on the minority class. A real-world scenario is medical diagnosis, where a model might have high overall accuracy but miss a rare disease; the F1-score for that disease class reveals the trade-off between false positives and false negatives.
KKey Concepts to Remember
- F1-score is the harmonic mean of precision and recall.
- It provides a balanced measure of a model's accuracy and completeness for a specific class.
- F1-score is particularly useful for evaluating models on imbalanced datasets.
- A higher F1-score indicates better performance, balancing false positives and false negatives.
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
F1-score is the harmonic mean of precision and recall.
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. F1-score is the harmonic mean of precision and recall. 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
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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 — F1-score is the harmonic mean of precision and recall..
What is the correct answer to this question?
The correct answer is: F1-score — The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both false positives and false negatives. Since the model has high overall accuracy but poor performance on the minority class (Iris virginica), the F1-score is ideal for evaluating the model's effectiveness on that class, as it accounts for class imbalance better than accuracy alone.
What should I do if I get this AI-900 question wrong?
Review f1-score is the harmonic mean of precision and recall., then practise related AI-900 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
F1-score is the harmonic mean of precision and recall.
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Last reviewed: Jun 11, 2026
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