Snowflake SnowPro Advanced: Data Scientist Certification : DSA-C03 Exam

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

A financial institution suspects fraudulent activity based on unusual transaction patterns. They want to use association rule mining to identify relationships between different transaction attributes (e.g., transaction amount, location, time of day, merchant category code) that are indicative of fraud. The data is stored in a Snowflake table called 'TRANSACTIONS'. Which of the following considerations are CRITICAL when applying association rule mining in this fraud detection scenario?

• A. Ensure that the Apriori algorithm is run directly within Snowflake using SQL to maximize performance and scalability, rather than extracting the data and processing it in an external Python environment.
• B. Ignore transaction attributes that have a large number of distinct values (e.g., specific location coordinates) as they will likely lead to an explosion of rules and make interpretation difficult.
• C. Focus solely on rules with very high support (e.g., > 0.1) to ensure statistical significance and avoid overfitting to rare fraudulent events.
• D. Carefully discretize continuous variables like 'transaction amount' and 'time of day' into meaningful categories to enable association rule mining, and consider the impact of different discretization strategies on the resulting rules.
• E. Prioritize rules with high confidence and lift, even if support is relatively low, as rare but highly predictive combinations of attributes can be strong indicators of fraudulent activity.

You have a Snowpark DataFrame named 'product_reviews' containing customer reviews for different products. The DataFrame includes columns like 'product_id' , 'review_text' , and 'rating'. You want to perform sentiment analysis on the 'review_text' to identify the overall sentiment towards each product. You decide to use Snowpark for Python to create a user-defined function (UDF) that utilizes a pre-trained sentiment analysis model hosted externally. You need to ensure secure access to this model and efficient execution. Which of the following represents the BEST approach, considering security and performance?

• A. Create an external function in Snowflake that calls a serverless function. Configure the API gateway in front of the serverless function to enforce authentication via Mutual TLS (mTLS) using Snowflake-managed certificates.
• B. Create a Snowpark Pandas UDF that calls the external sentiment analysis API. Use Snowflake secrets management to store the API key and retrieve it within the UDF.
• C. Create an inline Python UDF that directly calls the external sentiment analysis API with hardcoded API keys within the UDF code.
• D. Create an external function in Snowflake that calls a serverless function (e.g., AWS Lambda, Azure Function) that performs the sentiment analysis. Use Snowflake's network policies to restrict access to the serverless function and secrets management to handle API keys.
• E. Create a Java UDF that utilizes a library to call the sentiment analysis API. Pass the API key as a parameter to the UDF each time it is called.

You are tasked with forecasting the daily sales of a specific product for the next 30 days using Snowflake. You have historical sales data for the past 3 years, stored in a Snowflake table named 'SALES DATA', with columns 'SALE DATE (DATE type) and 'SALES AMOUNT' (NUMBER type). You want to use the Prophet library within a Snowflake User-Defined Function (UDF) for forecasting. The Prophet model requires the input data to have columns named 'ds' (for dates) and 'y' (for values). Which of the following code snippets demonstrates the CORRECT way to prepare and pass your data to the Prophet UDF in Snowflake, assuming you've already created the Python UDF 'prophet_forecast'?

• A.
  
• B.
  
• C.
  
• D.
  
• E.
  

You are validating a time series forecasting model for daily sales using Snowflake and Snowpark. The residuals plot shows a clear sinusoidal pattern. Which of the following actions should you consider to improve your model? (Select all that apply)

• A. Increase the regularization strength in your model.
• B. Remove outlier data points to improve overall model performance.
• C. Change the algorithm to a linear regression model, since it is more likely to capture sinusoidal patterns
• D. Incorporate lagged features representing previous sales values (e.g., sales from the previous day, week, or month).
• E. Apply a Box-Cox transformation to the target variable (sales) to stabilize the variance.