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Microsoft AI-900 Exam - Topic 1 Question 97 Discussion

What are two metrics that you can use to evaluate a regression model? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.
A) coefficient of determination (R2) and C) root mean squared error (RMSE)
B) F1 score
D) area under curve (AUC)
E) balanced accuracy

Microsoft AI-900 Exam - Topic 1 Question 97 Discussion

Actual exam question for Microsoft's AI-900 exam
Question #: 97
Topic #: 1
[All AI-900 Questions]

What are two metrics that you can use to evaluate a regression model? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

Show Suggested Answer Hide Answer
Suggested Answer: A, C

A: R-squared (R2), or Coefficient of determination represents the predictive power of the model as a value between -inf and 1.00. 1.00 means there is a perfect fit, and the fit can be arbitrarily poor so the scores can be negative.

C: RMS-loss or Root Mean Squared Error (RMSE) (also called Root Mean Square Deviation, RMSD), measures the difference between values predicted by a model and the values observed from the environment that is being modeled.


https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/metrics

by Vicky at Nov 25, 2025, 10:30 PM

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Lazaro
2 months ago
AUC and balanced accuracy are for different tasks.
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Mose
2 months ago
Yes, it’s for classification. R2 and RMSE make sense.
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Staci
2 months ago
F1 score isn’t relevant for regression, right?
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Refugia
2 months ago
RMSE gives us the error magnitude. Very useful!
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Henriette
2 months ago
Agreed! R2 shows how well the model fits.
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Eric
3 months ago
Agreed, R2 and RMSE are the way to go!
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Casie
3 months ago
I thought F1 score was relevant for regression too?
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Marcos
3 months ago
Wait, isn't AUC more for classification?
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Rickie
4 months ago
Definitely R2, but RMSE is better than AUC here.
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Willetta
4 months ago
R2 and RMSE are solid choices!
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Loren
4 months ago
I'm just glad they didn't ask about the Akaike Information Criterion. That one always trips me up!
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Alaine
4 months ago
B and D are more for classification models, not regression. Gotta read the question carefully!
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Dudley
4 months ago
I agree with Bulah. Those are the two main metrics I've seen used in my regression projects.
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Bulah
4 months ago
A and C are the correct answers. R2 and RMSE are commonly used metrics for evaluating regression models.
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Cherry
5 months ago
I feel like I might confuse RMSE with AUC since they both deal with model evaluation, but I think RMSE is the right choice for regression.
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Lyndia
5 months ago
I practiced a similar question where R2 and RMSE were the correct answers. I hope that applies here too!
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Malcolm
5 months ago
I think RMSE is definitely one of the metrics, but I can't recall if R2 is the other one or if there's something else I should consider.
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Daniel
5 months ago
I remember studying R2 as a key metric for regression, but I'm not entirely sure about the second one. Was it RMSE or something else?
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Judy
5 months ago
Ugh, I'm drawing a blank here. I know R2 is important, but the other options are throwing me off. I guess I'll just go with R2 and RMSE since those seem like the safest choices.
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Refugia
5 months ago
Hmm, let me think. R2 for sure, that's a no-brainer. But the other one... I'm torn between RMSE and AUC. I'll hedge my bets and go with R2 and RMSE.
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Rolande
6 months ago
Okay, I've got this. R2 and RMSE are definitely the way to go. Those are the go-to regression model metrics that we covered in class. I feel pretty confident about this one.
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Rasheeda
6 months ago
I think R2 and RMSE are the best choices.
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Yvonne
6 months ago
Haha, I almost picked E just because it sounds fancy. Balanced accuracy is for binary classification, not regression.
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Kent
7 months ago
Oof, I'm a bit unsure on this one. I know R2 is important, but I'm not totally sure about the other options. Maybe I'll just go with R2 and RMSE to play it safe.
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Sheridan
7 months ago
Hmm, this seems straightforward. I'd go with R2 and RMSE - those are the classic regression model evaluation metrics, right?
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Merissa
1 month ago
Exactly! Stick with R2 and RMSE for regression.
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Glendora
1 month ago
F1 score and AUC are more for classification, right?
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Dottie
2 months ago
RMSE gives a good idea of prediction error too.
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Cheryl
6 months ago
I agree, R2 and RMSE are definitely the go-to metrics.
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Rolande
7 months ago
Yes! R2 shows how well the model fits the data.
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