AI-900 MAE is a regression evaluation metric. Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✓

  On average, the model's predictions are $5,000 away from the actual prices.

  Why this is correct

  Mean Absolute Error (MAE) computes the average of the absolute differences between each predicted price and the corresponding actual price. A MAE of $5,000 means that when you sum the magnitudes of all prediction errors and divide by the number of houses, the typical error magnitude is $5,000. Because absolute values are used, over- and under-predictions don't cancel out; this value represents the expected absolute deviation per prediction.

• ✗

  The model is accurate 95% of the time.

  Why it's wrong here

  Accuracy is a classification metric that measures the proportion of correct predictions out of total predictions, which is not meaningful for regression tasks where predictions are continuous values. MAE does not report a percentage of correct predictions; it reports an average error magnitude in the same units as the target variable (dollars). Saying the model is 'accurate 95% of the time' would require defining a tolerance band for what counts as correct, but MAE makes no such threshold assumption.

  When this WOULD be correct

  In a classification model evaluation, if a question states 'The model achieves 95% accuracy on the test set', then option B would be correct: the model is accurate 95% of the time.

• ✗

  The model's predictions are within $5,000 of the actual prices for 50% of the houses.

  Why it's wrong here

  This option describes a percentile-based error statistic, such as the median absolute error, which reports the value below which 50% of the individual absolute errors fall. MAE, by contrast, is the arithmetic mean of all absolute errors and is sensitive to the presence of a few very large errors that pull the average upward. Knowing that half the predictions are within $5,000 does not imply the average error is $5,000; for instance, large errors on the other half could make the MAE much higher.

  When this WOULD be correct

  This option would be correct if the question stated: 'The model's predictions are within $5,000 of the actual prices for 50% of the houses.' This describes the median absolute error, which is a different metric.

• ✗

  The square root of the average squared error is $5,000.

  Why it's wrong here

  This is the definition of Root Mean Squared Error (RMSE), not Mean Absolute Error (MAE). RMSE calculates the square root of the mean of the squared differences between predictions and actual values, which gives disproportionate weight to large errors. MAE simply averages the absolute differences without squaring, so it treats all error magnitudes linearly and will always be less than or equal to RMSE for the same set of predictions.

  When this WOULD be correct

  If the question asked 'Which metric is defined as the square root of the average squared error?' or 'Which metric is most sensitive to large errors?', then RMSE (option D) would be correct.

Option-by-option analysis

Why each answer is right or wrong

Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.

Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”

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