The answer is AUC_weighted. This metric is best for an imbalanced binary classification dataset because it calculates the area under the ROC curve for each class separately and then averages them using weights proportional to the actual class support, meaning the majority class (95% negative) and minority class (5% positive) both contribute fairly to the final score. Unlike standard accuracy, which would be misleadingly high by simply predicting the majority class, AUC_weighted penalizes models that ignore the minority class, making it robust to severe imbalance. On the AI-900 exam, this tests your understanding of how Azure Automated Machine Learning handles skewed data; a common trap is choosing accuracy or AUC_macro, which treats all classes equally and can hide poor minority-class performance. Remember the memory tip: “Weighted for the weak” — AUC_weighted gives the minority class its fair share of influence, ensuring your model doesn’t just guess the majority.
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. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 using Azure Automated Machine Learning to build a binary classification model for a highly imbalanced dataset (95% negative, 5% positive). The data scientist wants AutoML to select the best model based on a metric that is robust to class imbalance. Which primary metric should the data scientist configure in the AutoML settings?
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.
Clue: "primary"
Why it matters: Asks for the main purpose or function, not a secondary benefit. Eliminate answers that describe side-effects or partial functions.
Why wrong: Accuracy is not robust to class imbalance because a model that predicts the majority class for all samples can achieve 95% accuracy while failing to detect any positive cases.
B
AUC_weighted
AUC_weighted calculates the area under the ROC curve and weights it by the prevalence of each class. It is robust to class imbalance and recommended for imbalanced datasets in AutoML.
C
F1_score
Why wrong: F1_score is a harmonic mean of precision and recall. While better than accuracy, it is not the default robust metric for imbalanced data in AutoML; AUC_weighted is preferred.
D
Log_loss
Why wrong: Log_loss measures the uncertainty of predictions. It is sensitive to calibration but not specifically designed to handle class imbalance.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
AUC_weighted
AUC_weighted is the correct primary metric for imbalanced binary classification because it computes the area under the ROC curve for each class and averages them with weight proportional to the class support. This weighting ensures that the metric reflects performance on both the majority (95% negative) and minority (5% positive) classes, making it robust to severe imbalance. Azure Automated Machine Learning uses AUC_weighted as a recommended metric when the dataset is skewed, as it penalizes models that ignore the minority class.
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 not robust to class imbalance because a model that predicts the majority class for all samples can achieve 95% accuracy while failing to detect any positive cases.
✓
AUC_weighted
Why this is correct
AUC_weighted calculates the area under the ROC curve and weights it by the prevalence of each class. It is robust to class imbalance and recommended for imbalanced datasets in AutoML.
Clue confirmation
The clue words "best", "primary" 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 a harmonic mean of precision and recall. While better than accuracy, it is not the default robust metric for imbalanced data in AutoML; AUC_weighted is preferred.
✗
Log_loss
Why it's wrong here
Log_loss measures the uncertainty of predictions. It is sensitive to calibration but not specifically designed to handle class imbalance.
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 on imbalanced datasets it can be misleadingly high and does not reflect minority class performance.
Detailed technical explanation
How to think about this question
Under the hood, AUC_weighted computes the ROC curve for each class (one-vs-rest) and averages the AUC values weighted by the number of true instances in each class. This weighting directly addresses imbalance because the minority class contributes proportionally to its small size, preventing the majority class from dominating the metric. In Azure AutoML, the primary metric drives model selection and hyperparameter tuning; using AUC_weighted ensures that the algorithm favors models that achieve good separation between positive and negative classes even when positives are rare, which is critical in scenarios like fraud detection or rare disease diagnosis.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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: AUC_weighted — AUC_weighted is the correct primary metric for imbalanced binary classification because it computes the area under the ROC curve for each class and averages them with weight proportional to the class support. This weighting ensures that the metric reflects performance on both the majority (95% negative) and minority (5% positive) classes, making it robust to severe imbalance. Azure Automated Machine Learning uses AUC_weighted as a recommended metric when the dataset is skewed, as it penalizes models that ignore the minority class.
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", "primary". 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
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 →
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.
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.