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Microsoft DP-100 Exam - Topic 3 Question 135 Discussion

Actual exam question for Microsoft's DP-100 exam
Question #: 135
Topic #: 3
[All DP-100 Questions]

You are analyzing a dataset containing historical data from a local taxi company. You arc developing a regression a regression model.

You must predict the fare of a taxi trip.

You need to select performance metrics to correctly evaluate the- regression model.

Which two metrics can you use? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

Show Suggested Answer Hide Answer
Suggested Answer: B, E

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model

by Martina at Jan 05, 2026, 05:16 AM

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Juliann
23 days ago
Exactly! F1 doesn't apply here. B and E are definitely the way to go.
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Lemuel
29 days ago
But what about A? F1 score is important for classification, not regression.
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Brent
1 month ago
Agreed! A low RMSE indicates better predictions.
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Edison
1 month ago
I think B and E are the best choices. R Squared close to 1 shows good fit.
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Kip
1 month ago
Totally agree, R Squared and low RMSE are the way to go!
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Willow
2 months ago
F1 score isn't even relevant for regression models.
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Leonie
2 months ago
Wait, why would you want a high RMSE? That sounds off.
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Santos
2 months ago
RMSE should be low for better predictions.
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Malcolm
2 months ago
R Squared close to 1 is definitely a good sign!
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Willard
2 months ago
B and E, easy peasy. Unless you want to end up in the back seat of the taxi.
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Solange
3 months ago
B and E are the way to go. Anything else and you're just driving in circles.
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Jaclyn
3 months ago
B and E for sure. Anything else and you might as well just guess the fares.
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Mel
3 months ago
I'd go with B and E. Gotta keep that R-squared high and the RMSE low!
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Annabelle
4 months ago
B and E are the correct metrics to evaluate the regression model.
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Francesco
4 months ago
I recall that a high R-Squared value is desirable, but I’m not sure if RMSE should be low or high. I guess I’ll pick B and E too.
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Carissa
4 months ago
I’m a bit confused because I thought F1 scores were more for classification problems. I’m leaning towards B and E, but I’m not entirely confident.
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Marg
4 months ago
I practiced similar questions, and I think a low Root Mean Square Error is definitely a good indicator of model performance, so I’d go with E.
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Horace
4 months ago
I remember that R-Squared is important for regression, so I think option B is a good choice. But I'm not sure about the second metric.
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Kirk
4 months ago
I'm leaning towards B and E as well. The R-Squared value close to 1 will show how much of the variance in the data the model is explaining, and a low RMSE will indicate the model is making accurate predictions. Seems like a solid strategy to me.
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Mattie
5 months ago
Okay, let me think this through. We want to predict the fare of a taxi trip, so we need metrics that tell us how well the model is performing. B and E sound like the right choices - high R-Squared and low RMSE. I feel pretty confident about those.
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Pearlene
5 months ago
Hmm, I'm a bit confused. I was thinking B and D might be good, but now I'm not sure. I'll need to double-check the definitions of these metrics to make sure I understand them correctly.
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Candida
5 months ago
I think I'd go with B and E - an R-Squared value close to 1 and a low Root Mean Square Error. Those seem like the most appropriate metrics to evaluate the regression model's performance.
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Zena
13 days ago
Definitely! A high R-Squared and low RMSE show good fit.
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Barbra
18 days ago
I agree, B and E are solid choices!
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