Question 1hardmultiple choice
Full question →hardmultiple choiceObjective-mapped
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?
Answer choices
Why each option matters
Good practice is not just finding the correct option. The wrong answers often show the exact trap the exam wants you to fall into.
A
Distractor review
Accuracy
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
Best answer
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
Distractor review
F1_score
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
Distractor review
Log_loss
Log_loss measures the uncertainty of predictions. It is sensitive to calibration but not specifically designed to handle class imbalance.
Common exam trap
Common exam trap: OSPF can fail even when IP connectivity looks correct
OSPF neighbour formation depends on matching areas, timers, network type, authentication and passive-interface behaviour. Do not choose an answer only because the devices can ping.
Technical deep dive
How to think about this question
OSPF questions usually test the details that control adjacency and route selection. Read the neighbour state, area, router ID and interface configuration before deciding what is wrong.
KKey Concepts to Remember
- OSPF neighbours must agree on key parameters.
- Router ID selection can affect neighbour relationships and LSDB output.
- OSPF cost influences the preferred path.
- A route can appear in OSPF information but not become the installed route.
TExam Day Tips
- Check area mismatch first when OSPF adjacency fails.
- Review passive interfaces when a network is advertised but no neighbour forms.
- Use show ip ospf neighbor and show ip route clues carefully.
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.
More questions from this exam
Keep practising from the same exam bank, or move into a focused topic page if this question exposed a weak area.
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FAQ
Questions learners often ask
What does this AI-900 question test?
OSPF neighbours must agree on key parameters.
What is the correct answer to this question?
The correct answer is: AUC_weighted — For imbalanced datasets, AUC_weighted is the recommended primary metric in Azure Automated Machine Learning because it calculates the area under the ROC curve weighted by class prevalence, making it robust to skew. Accuracy would be misleading (a model predicting all negative gets 95% accuracy), F1_score can be used but is not as robust, and log_loss focuses on prediction uncertainty.
What should I do if I get this AI-900 question wrong?
Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.
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