- A
Precision is the speed of prediction; recall is the model's memory usage
Why wrong: These terms describe computational resources — precision and recall are classification metrics measuring different types of accuracy.
- B
Precision measures correctness of positive predictions; recall measures coverage of actual positives
Precision = TP/(TP+FP): how often positive predictions are right. Recall = TP/(TP+FN): how many true positives were found.
- C
Precision and recall are both the same metric, just calculated on different datasets
Why wrong: Precision and recall are distinct metrics measuring different things — false positive rate vs. false negative rate respectively.
- D
Recall is higher than precision whenever the model has seen more training data
Why wrong: The relationship between precision and recall depends on the classification threshold and class balance, not just training data volume.
Quick Answer
The correct answer is that precision measures the correctness of positive predictions, while recall measures the coverage of actual positives. Precision is calculated as true positives divided by the sum of true positives and false positives, so it answers: of all the items your model labeled as positive, how many were actually correct? Recall, on the other hand, is true positives divided by the sum of true positives and false negatives, answering: of all the actual positive cases, how many did your model successfully find? On the Microsoft Azure AI-900 exam, this distinction often appears in scenario-based questions about imbalanced datasets—for example, when evaluating a fraud detection model where false positives are costly, you would prioritize precision, but for a medical diagnosis model where missing a positive case is dangerous, recall is more critical. A common trap is confusing the two because both focus on true positives; remember the memory tip: precision is about being precise with your predictions (avoiding false alarms), while recall is about recalling all the relevant items (not missing any).
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. 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 the difference between 'precision' and 'recall' as model evaluation metrics?
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
Precision measures correctness of positive predictions; recall measures coverage of actual positives
Option B is correct because precision measures the proportion of positive identifications that were actually correct (true positives / (true positives + false positives)), while recall measures the proportion of actual positives that were correctly identified (true positives / (true positives + false negatives)). In Azure Machine Learning, these metrics are critical for evaluating classification models, especially when dealing with imbalanced datasets, as they provide distinct insights into model performance.
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.
- ✗
Precision is the speed of prediction; recall is the model's memory usage
Why it's wrong here
These terms describe computational resources — precision and recall are classification metrics measuring different types of accuracy.
- ✓
Precision measures correctness of positive predictions; recall measures coverage of actual positives
Why this is correct
Precision = TP/(TP+FP): how often positive predictions are right. Recall = TP/(TP+FN): how many true positives were found.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Precision and recall are both the same metric, just calculated on different datasets
Why it's wrong here
Precision and recall are distinct metrics measuring different things — false positive rate vs. false negative rate respectively.
- ✗
Recall is higher than precision whenever the model has seen more training data
Why it's wrong here
The relationship between precision and recall depends on the classification threshold and class balance, not just training data volume.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse precision and recall with unrelated concepts like speed or memory, or assume they are identical metrics, when in fact they measure fundamentally different aspects of classification accuracy.
Detailed technical explanation
How to think about this question
Precision and recall are often inversely related, and adjusting the classification threshold in a model (e.g., in logistic regression or a neural network) can trade off one for the other. In Azure Machine Learning, the F1 score (harmonic mean of precision and recall) is commonly used to balance both metrics, especially in scenarios like fraud detection where both false positives and false negatives carry significant costs. Understanding this trade-off is essential for selecting the right threshold and evaluating model performance in production.
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
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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: Precision measures correctness of positive predictions; recall measures coverage of actual positives — Option B is correct because precision measures the proportion of positive identifications that were actually correct (true positives / (true positives + false positives)), while recall measures the proportion of actual positives that were correctly identified (true positives / (true positives + false negatives)). In Azure Machine Learning, these metrics are critical for evaluating classification models, especially when dealing with imbalanced datasets, as they provide distinct insights into model performance.
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
2 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 is precision in the context of binary classification model evaluation?
medium- A.The proportion of actual positives that the model correctly identified
- ✓ B.The proportion of positive predictions that are actually correct
- C.The overall proportion of all predictions that are correct
- D.The number of decimal places in the model's confidence score
Why B: Precision measures the accuracy of positive predictions: it is the ratio of true positives to the sum of true positives and false positives. Option B correctly defines this as 'the proportion of positive predictions that are actually correct,' which is the standard definition used in Azure Machine Learning's classification metrics.
Variation 2. What is recall (sensitivity) in the context of binary classification model evaluation?
medium- A.The proportion of positive predictions that are actually correct
- ✓ B.The proportion of actual positives that the model correctly identified
- C.The overall proportion of predictions that match the actual labels
- D.How quickly the model can be updated with new training data
Why B: Recall (sensitivity) measures the proportion of actual positive cases that the model correctly identifies. In binary classification, it answers: 'Of all the truly positive instances, how many did the model catch?' This is critical in scenarios where missing a positive (false negative) is costly, such as disease screening or fraud detection.
Last reviewed: Jun 11, 2026
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