Free Snowflake DSA-C03 Exam Dumps Questions & Answers

You are analyzing sales data in Snowflake using Snowpark to identify seasonality. You have a table named 'SALES DATA with columns 'SALE DATE (TIMESTAMP NTZ) and 'AMOUNT (NUMBER). You want to calculate the rolling average sales for each week over a period of 12 weeks using a Snowpark DataFrame. Which of the following Snowpark code snippets correctly implements this calculation?

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You are building a fraud detection model for an e-commerce platform. One of the features is 'purchase_amount', which ranges from $1 to $10,000. The data has a skewed distribution with many small purchases and a few very large ones. You need to normalize this feature for your model, which uses gradient descent. Which normalization technique(s) would be most suitable in Snowflake, considering the data characteristics and the need to handle potential future outliers?

• A.  Power Transformer (e.g., Yeo-Johnson) implemented with Snowpark Python:
  
• B.  Unit Vector normalization (L2 Normalization) using SQL:
  
• C.  Min-Max scaling using the following SQL:
  
• D.  Robust scaling using interquartile range (IQR) in a stored procedure with Python:
  
• E.  Z-score standardization using the following SQL:
  

A data scientist is analyzing website conversion rates for an e-commerce platform. They want to estimate the true conversion rate with 95% confidence. They have collected data on 10,000 website visitors, and found that 500 of them made a purchase. Given this information, and assuming a normal approximation for the binomial distribution (appropriate due to the large sample size), which of the following Python code snippets using scipy correctly calculates the 95% confidence interval for the conversion rate? (Assume standard imports like 'import scipy.stats as St' and 'import numpy as np').

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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.  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.
• B.  Over-sampling, combined with downsampling, makes the model more prone to overfitting since this causes the model to train on a large dataset.
• C.  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.
• D.  The optimal sampling ratio for both up-sampling and down-sampling must always be 1:1, regardless of the initial class distribution.
• E.  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.

You've built a model in Snowflake to predict house prices based on features like location, square footage, and number of bedrooms. After deploying the model, you want to ensure that the incoming data used for prediction is similar to the data the model was trained on. You decide to implement a data distribution comparison strategy. Consider these options and select all that apply:

• A.  Generate histograms for each numerical feature in both the training and incoming datasets using a Python UDF that leverages libraries like Pandas and Matplotlib. Visually compare the histograms to identify potential distribution shifts.
• B.  Use Snowflake's built-in statistics functions to compute quantiles (e.g., 25th, 50th, 75th percentiles) for each numerical feature. Compare these quantiles between the training and incoming datasets and set up alerts for significant deviations.
• C.  Calculate the mean and standard deviation for each numerical feature in both the training and incoming datasets using Snowflake SQL. Create a Snowflake Alert that triggers if the difference in means or standard deviations exceeds a predefined threshold for any feature.
• D.  Only focus on monitoring the target variable (house price) and assume that if the distribution of house prices remains stable, the input data distribution is also stable.
• E.  Create a binary classification model in Snowflake that attempts to predict whether a given row of data comes from the training dataset or the incoming dataset. If the model achieves high accuracy, it indicates a significant difference in data distributions.