- A
A. Precision for the default class
Why wrong: Precision is the fraction of positive predictions that are correct. Since the model makes no positive predictions, precision is undefined (division by zero) and not helpful.
- B
B. Recall for the default class
Recall (sensitivity) for defaults is the fraction of actual defaults that the model correctly identifies. With no defaults predicted, recall = 0%, clearly showing the model's failure.
- C
C. F1-score for the default class
Why wrong: F1-score is the harmonic mean of precision and recall. Since recall is 0, the F1-score is also 0, but recall alone already reveals the issue more directly.
- D
D. Overall accuracy
Why wrong: Accuracy is 95% because the model correctly predicts the majority class (non-default) for all cases. This high value masks the complete failure to detect defaults.
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. A key principle to apply: recall measures the proportion of actual positive cases correctly identified.. 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 trains a binary classification model to predict whether a loan applicant will default (positive class) or not (negative class). The training data contains 5% default cases. The model predicts 'no default' for every applicant in the test set and achieves 95% accuracy. Which evaluation metric best reveals that the model is failing to identify any default 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.
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
B. Recall for the default class
Recall for the default class (positive class) measures the proportion of actual default cases that the model correctly identifies. With a model that predicts 'no default' for every applicant, recall for the default class is 0% because it fails to identify any true positive cases. This metric directly reveals the model's inability to detect defaults, despite the high overall accuracy of 95%.
Key principle: Recall measures the proportion of actual positive cases correctly identified.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A. Precision for the default class
Why it's wrong here
Precision is the fraction of positive predictions that are correct. Since the model makes no positive predictions, precision is undefined (division by zero) and not helpful.
- ✓
B. Recall for the default class
Why this is correct
Recall (sensitivity) for defaults is the fraction of actual defaults that the model correctly identifies. With no defaults predicted, recall = 0%, clearly showing the model's failure.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Recall measures the proportion of actual positive cases correctly identified.
- ✗
C. F1-score for the default class
Why it's wrong here
F1-score is the harmonic mean of precision and recall. Since recall is 0, the F1-score is also 0, but recall alone already reveals the issue more directly.
- ✗
D. Overall accuracy
Why it's wrong here
Accuracy is 95% because the model correctly predicts the majority class (non-default) for all cases. This high value masks the complete failure to detect defaults.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often focus on the high overall accuracy (95%) and assume the model is performing well, overlooking how class imbalance can make accuracy a misleading metric, and fail to recognize that recall for the positive class is the appropriate diagnostic tool.
Detailed technical explanation
How to think about this question
Recall, also known as sensitivity or true positive rate, is calculated as TP / (TP + FN). In this case, with zero true positives and 5% false negatives (the actual defaults), recall is 0. This metric is critical in imbalanced classification problems where the positive class is rare, as it directly quantifies the model's ability to capture positive instances. In Azure Machine Learning, recall is a key metric for binary classification models, especially when the cost of missing a positive case (e.g., loan default) is high.
KKey Concepts to Remember
- Recall measures the proportion of actual positive cases correctly identified.
- Recall is crucial for imbalanced datasets, especially for the minority class.
- A recall of 0% for the positive class indicates the model missed all actual positives.
- Recall is calculated as True Positives / (True Positives + False Negatives).
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
Recall measures the proportion of actual positive cases correctly identified.
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. Recall measures the proportion of actual positive cases correctly identified. 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.
Review recall measures the proportion of actual positive cases correctly identified., then practise related AI-900 questions on the same topic to reinforce the concept.
- →
Describe fundamental principles of machine learning on Azure — study guide chapter
Learn the concepts, then practise the questions
- →
Describe fundamental principles of machine learning on Azure practice questions
Targeted practice on this topic area only
- →
All AI-900 questions
1,020 questions across all exam domains
- →
Microsoft Azure AI Fundamentals AI-900 study guide
Full concept coverage aligned to exam objectives
- →
AI-900 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Describe Artificial Intelligence workloads and considerations practice questions
Practise AI-900 questions linked to Describe Artificial Intelligence workloads and considerations.
Describe fundamental principles of machine learning on Azure practice questions
Practise AI-900 questions linked to Describe fundamental principles of machine learning on Azure.
Describe features of computer vision workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of computer vision workloads on Azure.
Describe features of Natural Language Processing workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of Natural Language Processing workloads on Azure.
Describe features of generative AI workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of generative AI workloads on Azure.
AI-900 fundamentals practice questions
Practise AI-900 questions linked to AI-900 fundamentals.
AI-900 scenario practice questions
Practise AI-900 questions linked to AI-900 scenario.
AI-900 troubleshooting practice questions
Practise AI-900 questions linked to AI-900 troubleshooting.
Practice this exam
Start a free AI-900 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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 — Recall measures the proportion of actual positive cases correctly identified..
What is the correct answer to this question?
The correct answer is: B. Recall for the default class — Recall for the default class (positive class) measures the proportion of actual default cases that the model correctly identifies. With a model that predicts 'no default' for every applicant, recall for the default class is 0% because it fails to identify any true positive cases. This metric directly reveals the model's inability to detect defaults, despite the high overall accuracy of 95%.
What should I do if I get this AI-900 question wrong?
Review recall measures the proportion of actual positive cases correctly identified., then practise related AI-900 questions on the same topic to reinforce the concept.
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?
Recall measures the proportion of actual positive cases correctly identified.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
More AI-900 practice questions
- A company deploys an AI system to screen job applications. The system is a complex neural network that learns patterns f…
- What is 'model versioning' and why is it essential in MLOps?
- What is 'AI transparency' in Microsoft's Responsible AI principles?
- A company uses Azure OpenAI Service to generate marketing copy. They notice that sometimes the generated text contains r…
- A data scientist is training a regression model to predict house prices using features like square footage, number of be…
- A company uses Azure OpenAI Service to generate marketing copy. They want to ensure that the generated text does not con…
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.