Free Snowflake DSA-C03 Exam Dumps Questions & Answers

You're building a regression model using Snowpark Python to predict house prices. After initial training, you observe that the model consistently overestimates the prices of high-value houses and underestimates the prices of low-value houses. Given the options below, which optimization metric, along with code snippet to calculate it using Snowpark, would be most effective in addressing this specific issue?

• A.Root Mean Squared Error (RMSE) - as it gives more weight to larger errors, making it suitable for addressing the underestimation/overestimation problem.
  
• B.Mean Squared Error (MSE) - as it is less sensitive to outliers than RMSE.
  
• C.R-squared - as it measures the proportion of variance explained, directly addressing how well the model fits the data across all price ranges.
  
• D.Mean Absolute Error MAE - as it is sensitive to outliers and will penalize large errors more heavily.
  
• E.Adjusted R-squared - as it penalizes the addition of irrelevant features, improving the model's generalization ability.
  

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.Z-score standardization using the following SQL:
  
• 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.Power Transformer (e.g., Yeo-Johnson) implemented with Snowpark Python:
  

You are performing exploratory data analysis on a large sales dataset in Snowflake using Snowpark. The dataset contains columns such as 'order_id', , and 'profit'. You want to identify the top 5 most profitable products for each month. You have already created a Snowpark DataFrame named 'sales_df. Which of the following Snowpark operations, when combined correctly, will efficiently achieve this?

• A.First, create a temporary table with aggregated monthly profit for each product using SQL. Then, use Snowpark to read the temporary table and apply a window function partitioned by ordered by 'sum(profit) DESC'.
• B.Group by and 'product_id' , aggregate 'sum(profit)' , then use partitioned by ordered by 'sum(profit) DESC'.
• C.Use 'ntile(5)' partitioned by ordered by 'sum(profit) DESC' after grouping by and 'product_id', and aggregating 'sum(profit)'.
• D.Group by 'product_id', aggregate 'sum(profity, then use partitioned by ordered by 'sum(profit) DESC' within a UDF.
• E.Use 'rank()' partitioned by ordered by 'sum(profit) DESC' , after grouping by and 'product_id' , and aggregating 'sum(profity.

You are building a machine learning model to predict customer churn for a telecommunications company. One of the features is 'tariff_plan', which is a string representing different tariff plans (e.g., 'Basic', 'Premium', 'Unlimited'). You need to encode this feature for your model, but you also want to handle potential new tariff plans that might appear in future data'. Which encoding method and Snowflake SQL approach would be MOST suitable to minimize dimensionality and address unseen values effectively, assuming the number of plans is moderately high (around 20-30)?

• A.Label Encoding using a UDF (User-Defined Function) with a predefined mapping, assigning a new integer to unseen values, and storing the mapping in a separate table in Snowflake.
• B.Hash Encoding (Feature Hashing) using a UDF in Snowflake, with a fixed number of features and a hashing function to map each tariff plan to a feature index, accepting potential collisions. Handle new tariff plans naturally through the hashing function.
• C.One-Hot Encoding using CREATE OR REPLACE VIEW, handling new values by NULLIF('Unknown', tariff_plan) before encoding, potentially leading to a high number of columns.
• D.Binary Encoding using a UDF to convert each tariff plan into binary code, storing encoded results into snowflake, then splitting the binary representation into separate columns.
• E.Target Encoding (Mean Encoding) using Snowflake SQL, calculating the mean churn rate for each tariff plan and using that as the encoded value. Handle unseen values with the global mean churn rate, being mindful of potential target leakage.

A marketing analyst at 'NovaRetail' suspects that a new advertising campaign has increased the average purchase amount. They have historical purchase data in a Snowflake table called 'purchase_historf. To validate their hypothesis using the Central Limit Theorem (CLT), they perform the following steps: 1. Calculate the population mean (?) of purchase amounts from the historical data'. 2. Draw 500 random samples of size 50 from the table. 3. Calculate the sample mean (x?) for each sample. Which of the following steps are essential for correctly applying the Central Limit Theorem to perform a z-test to determine whether the new advertising campaign has significantly increased the average purchase amount?

• A.Calculate the standard deviation of the sample means and use it as an estimate for the standard error of the mean.
• B.Ensure that the samples are drawn independently and randomly.
• C.Verify that the sample size (n=50) is sufficiently large to approximate normality of the sample mean distribution based on the CLT. This implicitly assumes population size is significantly larger than the sample size.
• D.Check if the original population distribution (purchase amounts) is approximately normally distributed.
• E.Calculate the standard deviation of the population (?) from the historical data and estimate the standard error of the mean as ? / sqrt(50).