- A
Accuracy
Why wrong: Accuracy can be misleading in imbalanced datasets because a model that always predicts the majority class can achieve high accuracy while having poor recall for the minority class.
- B
Precision
Why wrong: Precision measures how many of the predicted positive cases are actually positive. It focuses on false positives, not false negatives, so it does not directly address the goal of minimizing false negatives.
- C
Recall
Recall measures the fraction of actual positive cases that were correctly predicted. Prioritizing recall helps minimize false negatives, which is the stated goal.
- D
F1 Score
Why wrong: F1 Score is the harmonic mean of precision and recall. While it is useful when both false positives and false negatives are important, it is not as directly aligned with minimizing false negatives as recall alone.
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 building a binary classification model to predict fraudulent credit card transactions. The dataset is highly imbalanced: only 1% of transactions are fraudulent. The cost of a false negative is very high because missing a fraudulent transaction can lead to significant financial loss. Which evaluation metric should the data scientist prioritize to minimize 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 (also known as sensitivity or true positive rate) measures the proportion of actual positive cases (fraudulent transactions) that are correctly identified. In this highly imbalanced scenario where missing a fraud (false negative) is extremely costly, maximizing recall ensures that the model catches as many fraudulent transactions as possible, even if it means some false positives occur. This directly aligns with the goal of minimizing false negatives.
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 can be misleading in imbalanced datasets because a model that always predicts the majority class can achieve high accuracy while having poor recall for the minority class.
- ✗
Precision
Why it's wrong here
Precision measures how many of the predicted positive cases are actually positive. It focuses on false positives, not false negatives, so it does not directly address the goal of minimizing false negatives.
- ✓
Recall
Why this is correct
Recall measures the fraction of actual positive cases that were correctly predicted. Prioritizing recall helps minimize false negatives, which is the stated goal.
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.
- ✗
F1 Score
Why it's wrong here
F1 Score is the harmonic mean of precision and recall. While it is useful when both false positives and false negatives are important, it is not as directly aligned with minimizing false negatives as recall alone.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose Accuracy because it is the most intuitive metric, failing to recognize that in imbalanced datasets with high false-negative cost, recall is the critical measure to minimize missed positives.
Detailed technical explanation
How to think about this question
Recall is calculated as TP / (TP + FN), where FN represents false negatives. In fraud detection, a low recall means many fraudulent transactions slip through, leading to financial losses. Under the hood, optimizing recall often involves lowering the decision threshold of the model (e.g., from 0.5 to 0.3) to classify more instances as positive, which increases recall at the expense of precision. In Azure Machine Learning, you can use the 'Recall' metric in automated ML or set a custom metric via a scoring function to guide model selection.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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: Recall — Recall (also known as sensitivity or true positive rate) measures the proportion of actual positive cases (fraudulent transactions) that are correctly identified. In this highly imbalanced scenario where missing a fraud (false negative) is extremely costly, maximizing recall ensures that the model catches as many fraudulent transactions as possible, even if it means some false positives occur. This directly aligns with the goal of minimizing false negatives.
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.
About these practice questions
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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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