The answer is Recall. This metric is the correct choice because recall, also known as sensitivity, measures the proportion of actual positive cases that the model correctly identifies, and in this imbalanced classification scenario where the model always predicts 'no disease', recall drops to 0%—directly exposing the failure to catch any rare disease cases despite the misleading 99% accuracy. On the Microsoft Azure AI Fundamentals AI-900 exam, this tests your understanding that accuracy is a poor metric for imbalanced datasets; the common trap is to assume high accuracy means good performance, but recall reveals the model's inability to detect the minority class. A simple memory tip: Recall asks, "Of all the actual positives, how many did I catch?"—think of it as the "catch rate" for the rare but critical class.
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 medical research team trains a model to detect a rare disease from lab results. The disease occurs in only 1% of patients. The model predicts 'no disease' for every patient and achieves 99% accuracy. Which metric best reveals that the model is failing to identify actual disease cases?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Why wrong: Accuracy is 99% here, which hides the model's inability to detect the disease.
B
Precision
Why wrong: Precision is the ratio of true positives to all positive predictions. Since the model never predicts positive, precision is undefined or 0, but recall is the more direct measure of missed positives.
C
Recall
Recall (sensitivity) is the ratio of true positives to all actual positives. Here recall is 0%, clearly showing the model fails to identify any disease cases.
D
F1 score
Why wrong: F1 score is the harmonic mean of precision and recall. Since recall is 0, F1 is 0, but recall alone already reveals the failure.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Recall
Recall (sensitivity) measures the proportion of actual positive cases correctly identified by the model. With a 99% accuracy but zero true positives (since the model always predicts 'no disease'), recall is 0%, which directly reveals the model's failure to detect any actual disease cases. In Azure Machine Learning, recall is a key metric for imbalanced classification tasks, especially when missing a positive case has severe consequences.
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 is 99% here, which hides the model's inability to detect the disease.
✗
Precision
Why it's wrong here
Precision is the ratio of true positives to all positive predictions. Since the model never predicts positive, precision is undefined or 0, but recall is the more direct measure of missed positives.
✓
Recall
Why this is correct
Recall (sensitivity) is the ratio of true positives to all actual positives. Here recall is 0%, clearly showing the model fails to identify any disease cases.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
F1 score
Why it's wrong here
F1 score is the harmonic mean of precision and recall. Since recall is 0, F1 is 0, but recall alone already reveals the failure.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates see 99% accuracy and assume the model is performing well, without recognizing that accuracy is meaningless when the class distribution is extremely skewed.
Detailed technical explanation
How to think about this question
In binary classification with severe class imbalance (e.g., 1% positive), accuracy is a poor metric because it can be inflated by always predicting the majority class. Recall is calculated as TP / (TP + FN); here TP=0 and FN=all actual positives, so recall=0. Azure Machine Learning's automated ML provides a 'primary metric' option that can be set to 'Recall' for such scenarios, and it also supports 'weighted' or 'macro' averaging to handle imbalance. In production, a model with zero recall would miss every disease case, potentially leading to catastrophic outcomes in healthcare.
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: Recall — Recall (sensitivity) measures the proportion of actual positive cases correctly identified by the model. With a 99% accuracy but zero true positives (since the model always predicts 'no disease'), recall is 0%, which directly reveals the model's failure to detect any actual disease cases. In Azure Machine Learning, recall is a key metric for imbalanced classification tasks, especially when missing a positive case has severe consequences.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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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 the F1 score in machine learning evaluation?
medium
A.The first evaluation metric calculated before training a model
✓ B.The harmonic mean of precision and recall that balances both metrics
C.The proportion of predictions correct on the test set
D.A measure of how fast the model produces predictions
Why B: Option B is correct because the F1 score is defined as the harmonic mean of precision and recall, calculated as 2 * (precision * recall) / (precision + recall). This metric provides a single score that balances both false positives and false negatives, making it especially useful when classes are imbalanced. In Azure Machine Learning, the F1 score is a standard evaluation metric for classification models, reported in automated ML runs and designer modules.
Last reviewed: Jun 11, 2026
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