- A
Precision
Why wrong: Precision = TP/(TP+FP) measures how many of the predicted frauds are actually fraudulent. While important, it does not directly address missed frauds (false negatives).
- B
Recall
Recall measures the ability to find all actual positive cases. A high recall ensures that very few fraudulent transactions are missed, directly aligning with the goal of minimizing false negatives.
- C
Accuracy
Why wrong: Accuracy = (942+8)/1000 = 95.0%. However, because the dataset is highly imbalanced, accuracy can be high even if the model misses many frauds, so it is not a reliable metric for this goal.
- D
Specificity
Why wrong: Specificity = TN/(TN+FP) measures the true negative rate. It focuses on legitimate transactions, not on finding frauds, so it does not help minimize missed frauds.
Quick Answer
The answer is recall, because the business goal is to minimize false negatives—fraudulent transactions the model incorrectly labels as legitimate. Recall, also known as sensitivity, directly measures the proportion of actual positives correctly identified, calculated as true positives divided by the sum of true positives and false negatives. In this scenario, with only 2 missed fraudulent transactions out of 10 actual frauds, recall equals 0.80, and prioritizing this metric ensures the model catches as many frauds as possible. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your ability to match business requirements to the appropriate evaluation metric, often appearing in scenario-based questions where you must distinguish recall from precision or accuracy. A common trap is choosing accuracy (99.2% here) because it looks high, but it hides the costly missed frauds. To minimize false negatives, always think recall—remember the mnemonic “Recall catches the real crooks.”
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 evaluating a binary classification model that predicts whether a transaction is fraudulent. The test set contains 1,000 transactions: 990 legitimate and 10 fraudulent. The model's predictions are shown in the confusion matrix below. Confusion matrix: Predicted Legitimate Predicted Fraudulent Actual Legitimate 942 48 Actual Fraudulent 2 8 Which metric should the data scientist prioritize if the business goal is to minimize the number of fraudulent transactions that are missed (false negatives)?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
Recall
Recall (sensitivity) measures the proportion of actual positives correctly identified, calculated as TP/(TP+FN). With 2 false negatives (missed fraudulent transactions), recall is 8/(8+2)=0.80. Minimizing missed fraud directly corresponds to maximizing recall, making it the correct priority for this business goal.
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
Why it's wrong here
Precision = TP/(TP+FP) measures how many of the predicted frauds are actually fraudulent. While important, it does not directly address missed frauds (false negatives).
- ✓
Recall
Why this is correct
Recall measures the ability to find all actual positive cases. A high recall ensures that very few fraudulent transactions are missed, directly aligning with the goal of minimizing false negatives.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Accuracy
Why it's wrong here
Accuracy = (942+8)/1000 = 95.0%. However, because the dataset is highly imbalanced, accuracy can be high even if the model misses many frauds, so it is not a reliable metric for this goal.
- ✗
Specificity
Why it's wrong here
Specificity = TN/(TN+FP) measures the true negative rate. It focuses on legitimate transactions, not on finding frauds, so it does not help minimize missed frauds.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often pick Accuracy because it seems intuitive, but the severe class imbalance (99% legitimate) makes accuracy a poor metric, while Recall directly addresses the business requirement of minimizing missed fraud.
Detailed technical explanation
How to think about this question
In binary classification, recall is also called sensitivity or true positive rate. The confusion matrix shows 2 false negatives (actual fraudulent predicted legitimate), which are the most costly errors when the goal is to catch all fraud. In Azure Machine Learning, the 'Recall' metric is directly available in the evaluation results for classification models, and you can optimize for it using automated ML's primary metric setting or by adjusting the classification threshold to trade off precision and recall.
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
Got this wrong? Here's your next step.
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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: Recall — Recall (sensitivity) measures the proportion of actual positives correctly identified, calculated as TP/(TP+FN). With 2 false negatives (missed fraudulent transactions), recall is 8/(8+2)=0.80. Minimizing missed fraud directly corresponds to maximizing recall, making it the correct priority for this business goal.
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: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.
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