Question 1easymultiple choice
Full question →easymultiple choiceObjective-mapped
A data scientist uses Azure Machine Learning to train a model that predicts the electricity consumption (in kilowatt-hours) of a building based on features like building age, square footage, and number of occupants. The data scientist wants to evaluate how accurately the model's predictions match the actual consumption values. Which evaluation metric is most appropriate for this regression task?
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
Precision
Precision is a classification metric that measures the proportion of true positive predictions among all positive predictions; it is not suitable for regression.
B
Best answer
Mean Absolute Error (MAE)
MAE is a standard regression metric that measures the average absolute difference between predicted and actual values, making it appropriate for evaluating prediction accuracy.
C
Distractor review
F1 score
F1 score is a classification metric that combines precision and recall; it is not applicable to regression problems.
D
Distractor review
Area Under the ROC Curve (AUC)
AUC is a classification metric that evaluates the trade-off between true positive rate and false positive rate; it is not used for regression.
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: Mean Absolute Error (MAE) — Mean Absolute Error (MAE) measures the average magnitude of errors in a regression model, calculated as the average of absolute differences between predicted and actual values. Precision, F1 score, and Area Under the ROC Curve (AUC) are metrics used for classification tasks, not regression.
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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