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 training a binary classification model to detect fraudulent transactions. The dataset contains only 1% fraudulent transactions. The model achieves 99% accuracy on the test set, but when deployed, it fails to detect most actual fraud cases. Which metric would best reveal this issue?
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 misleading in imbalanced datasets because a model that never predicts fraud can still show high accuracy.
B
Precision
Why wrong: Precision measures how many of the predicted fraud cases are actually correct, but it doesn't show how many real fraud cases were missed.
C
Recall
Recall measures the fraction of actual fraud cases that the model correctly identifies. A low recall reveals the model's failure to detect fraud.
D
F1 score
Why wrong: F1 score combines precision and recall; while it gives a balanced view, recall alone is the most direct metric to expose the model's inability to catch fraud cases.
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. In this highly imbalanced dataset (1% fraud), a model can achieve 99% accuracy by simply predicting 'non-fraud' for every transaction, which yields zero true positives. Recall reveals this failure because it focuses solely on how many fraudulent transactions were caught, ignoring the vast majority of non-fraud cases.
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 misleading in imbalanced datasets because a model that never predicts fraud can still show high accuracy.
✗
Precision
Why it's wrong here
Precision measures how many of the predicted fraud cases are actually correct, but it doesn't show how many real fraud cases were missed.
✓
Recall
Why this is correct
Recall measures the fraction of actual fraud cases that the model correctly identifies. A low recall reveals the model's failure to detect fraud.
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 combines precision and recall; while it gives a balanced view, recall alone is the most direct metric to expose the model's inability to catch fraud cases.
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, failing to recognize that accuracy is a poor metric for imbalanced datasets, and that recall specifically measures the ability to detect the minority class.
Trap categories for this question
Command / output trap
Accuracy is misleading in imbalanced datasets because a model that never predicts fraud can still show high accuracy.
Detailed technical explanation
How to think about this question
In binary classification with severe class imbalance, accuracy is dominated by the majority class. Recall is calculated as TP / (TP + FN), and a model that always predicts the majority class will have recall = 0. This is why recall is the standard metric for fraud detection, medical screening, and other rare-event detection tasks. In Azure Machine Learning, you can monitor recall during training and set a custom metric threshold to ensure the model captures at least a minimum percentage of positive cases before deployment.
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. In this highly imbalanced dataset (1% fraud), a model can achieve 99% accuracy by simply predicting 'non-fraud' for every transaction, which yields zero true positives. Recall reveals this failure because it focuses solely on how many fraudulent transactions were caught, ignoring the vast majority of non-fraud cases.
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.
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Question Discussion
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