A data scientist is building a model to predict customer lifetime value (regression). She has a dataset of 100,000 customers. She wants to evaluate the model's performance reliably. Which approach is most appropriate?

Use a single random train-test split and report the R-squared.

Train on 100% of the data and report the training error.

Train on 80% of the data and test on the remaining 20%.

Use 5-fold cross-validation and report the average RMSE.

A binary classification model has an accuracy of 95% on a dataset where 95% of the instances belong to the negative class. What is the most important insight from this evaluation result?

The model is performing well because accuracy is high.

The model is likely overfitting the training data.

The model has a high false positive rate.

The accuracy metric may be misleading due to class imbalance.

In holdout validation, what is the correct way to use the validation set?

Use it to augment the training data after model selection.

Use it to tune hyperparameters and select the best model.

Use it only once at the very end to report final performance.

A data scientist has trained two regression models and wants to compare their performance. Which practice is most appropriate for a fair comparison?

Evaluate both models on the same held-out test set.

Evaluate each model on a different random sample of the data.

Choose the model with the lower RMSE on the training set.

SnowflakeSnowPro Advanced — Data Scientist

Domain 4Objective 2

Evaluate Model Performance SNOWPRO-ADVANCED-DATA-SCIENTIST Practice Questions (Page 3)

Part of the Model Development domain, which makes up ~19% of our current practice bank.

18questions here

4free pages

5concepts

Questions 11–15

11application · medium
A data scientist is building a model to predict customer lifetime value (regression). She has a dataset of 100,000 customers. She wants to evaluate the model's performance reliably. Which approach is most appropriate?
Select an answer first
12foundation · easy
A binary classification model has an accuracy of 95% on a dataset where 95% of the instances belong to the negative class. What is the most important insight from this evaluation result?
Select an answer first
13foundation · easy
In holdout validation, what is the correct way to use the validation set?
Select an answer first
14foundation · easy
A data scientist has trained two regression models and wants to compare their performance. Which practice is most appropriate for a fair comparison?
Select an answer first
15foundation · easy
Which metric summarizes the trade-off between the true positive rate and the false positive rate across all classification thresholds?
Select an answer first

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