Exam DSA-C03 Topic 1 Question 117 Discussion

Actual exam question for Snowflake's DSA-C03 exam
Question #: 117
Topic #: 1

You're developing a model to predict equipment failure using sensor data stored in Snowflake. The dataset is highly imbalanced, with failure events (positive class) being rare compared to normal operation (negative class). To improve model performance, you're considering both up-sampling the minority class and down-sampling the majority class. Which of the following statements regarding the potential benefits and drawbacks of combining up-sampling and down-sampling techniques in this scenario are TRUE? (Select TWO)

A. Combining up-sampling and down-sampling can lead to a more balanced dataset, potentially improving the model's ability to learn patterns from both classes without introducing excessive bias from solely up-sampling. B. Using both up-sampling and down-sampling always guarantees improved model performance compared to using only one of these techniques, regardless of the dataset characteristics. C. Down-sampling, when combined with up-sampling, can exacerbate the risk of losing important information from the majority class, leading to underfitting, especially if the majority class is already relatively small. D. Over-sampling, combined with downsampling, makes the model more prone to overfitting since this causes the model to train on a large dataset. E. The optimal sampling ratio for both up-sampling and down-sampling must always be 1:1, regardless of the initial class distribution.

Suggested Answer: A,C Vote an answer

Option A is correct: Combining both techniques can lead to a more balanced dataset, potentially improving the model's ability to learn patterns from both classes, if done correctly. Option C is correct: Down-sampling can exacerbate the risk of losing important information from the majority class, potentially leading to underfitting, especially if the majority class is already relatively small. Option B is incorrect because the effect depends on the data. Option D is incorrect because oversampling helps the model, even combined with downsampling, not to be prone to overfitting. Option E is incorrect because the right up/down-sampling ratio is very specific to the dataset.

by Mortimer at Mar 12, 2026, 08:29 AM

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