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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are tasked with building a model to predict customer churn. You have a table named in Snowflake with the following relevant columns: 'customer_id', 'login_date', , 'orders_placed', , and 'churned' (binary indicator). You want to engineer features that capture customer engagement over time using Snowpark for Python. Which of the following feature engineering steps, applied sequentially, are MOST effective in creating features indicative of churn risk?

A) 1. Calculate the number of days since the customer's last login, and use nulls instead of negative numbers to indicate inactivity. 2. Calculate the rolling 7-day average of 'orders_placed' using a window function, partitioning by 'customer_id' and ordering by 'login_date'. 3. Calculate the slope of a linear regression of page_views' over time for each customer, indicating the trend in engagement using Snowpark ML. 4. Calculate the percentage of weeks the customer logged in. 5. Create a feature showing standard deviation of page_views per customer over the last 90 days.
B) 1. Calculate the total 'page_views' and 'orders_placed' for each customer without considering time. 2. Use one-hot encoding for the 'subscription_type' column.
C) 1. Calculate the average 'page_views' per week for each customer over the last 3 months using a window function. 2. Calculate the recency of the last order (days since last order) for each customer. 3. Create a feature indicating the change in average daily page views over the last month compared to the previous month. 4. Create a feature showing standard deviation of page_views per customer over the last 90 days.
D) 1. Calculate the maximum 'page_views' in a single day for each customer. 2. Calculate the total number of days with no 'login_date' for each customer. 3. Create a feature indicating if a customer has ever placed an order. 4. Use a simple boolean for the 'subscription_type' column.
E) 1. Calculate the average 'page_views' per day for each customer. 2. Calculate the total number of for each customer. 3. Create a feature indicating whether the customer has a premium subscription ('subscription_type' = 'premium').

2. You are developing a fraud detection model in Snowflake. You've identified that transaction amounts and transaction frequency are key features. You observe that the transaction amounts are heavily right-skewed and the transaction frequencies have outliers. Furthermore, the model needs to be robust against seasonal variations in transaction frequency. Which of the following feature engineering steps, when applied in sequence, would be MOST appropriate to handle these data characteristics effectively?

A) 1. Apply min-max scaling to the transaction amounts. 2. Remove outliers in transaction frequency using the Interquartile Range (IQR) method. 3. Calculate the cumulative sum of transaction frequencies.
B) 1. Apply a logarithmic transformation to the transaction amounts. 2. Apply a Winsorization technique to the transaction frequencies to handle outliers. 3. Calculate a rolling average of transaction frequency over a 7-day window.
C) 1. Apply a square root transformation to the transaction amounts. 2. Standardize the transaction frequencies using Z-score normalization. 3. Create dummy variables for the day of the week.
D) 1. Apply a logarithmic transformation to the transaction amounts. 2. Replace outliers in transaction frequency with the mean value. 3. Create lag features of transaction frequency for the previous 7 days.
E) 1. Apply a Box-Cox transformation to the transaction amounts. 2. Apply a quantile-based transformation (e.g., using NTILE) to the transaction frequencies to map them to a uniform distribution. 3. Calculate the difference between the current transaction frequency and the average transaction frequency for that day of the week over the past year.

3. A data science team is tasked with deploying a pre-built anomaly detection model in Snowflake to identify fraudulent transactions. They need to use Snowflake ML functions and a Snowflake Native App (that houses the model) to achieve this. The Snowflake Native App is installed and available. The transaction data is stored in a table called 'TRANSACTIONS. Which of the following steps are essential to successfully deploy and use this pre-built model within a User Defined Function (UDF) for real-time scoring, assuming the app provides a function named 'ANOMALY SCORE?

A) Create a UDF that calls the 'ANOMALY _ SCORE function provided by the Snowflake Native App, passing the relevant transaction features as arguments.
B) Ensure the 'TRANSACTIONS' table is shared with the Snowflake Native App's container so the model can directly access the data.
C) Grant the USAGE privilege on the Snowflake Native App to the role executing the UDF. This ensures the UDF can access the app's functionality.
D) Create an external function in API Integration instead of UDF.
E) Train the pre-built anomaly detection model using Snowflake's ML functions (e.g., 'CREATE MODELS) with the 'TRANSACTIONS' data before creating the UDE

4. Your team has deployed a machine learning model to Snowflake for predicting customer churn. You need to implement a robust metadata tagging strategy to track model lineage, performance metrics, and usage. Which of the following approaches are the MOST effective for achieving this within Snowflake, ensuring seamless integration with model deployment pipelines and facilitating automated retraining triggers based on data drift?

A) Leveraging a third-party metadata management tool that integrates with Snowflake and provides a centralized repository for model metadata, lineage tracking, and data governance. This tool should support automated tag propagation and data drift monitoring. Use Snowflake external functions to trigger alerts based on metadata changes.
B) Relying solely on manual documentation and spreadsheets to track model metadata, as automated solutions introduce unnecessary complexity and potential errors.
C) Storing model metadata in a separate relational database (e.g., PostgreSQL) and using Snowflake external tables to access the metadata information. Implement custom stored procedures to synchronize metadata between Snowflake and the external database.
D) Utilizing Snowflake's INFORMATION SCHEMA views to extract metadata about tables, views, and stored procedures, and then writing custom SQL scripts to generate reports and track model lineage. Combine this with Snowflake's data masking policies to control access to sensitive metadata.
E) Using Snowflake's built-in tag functionality to tag tables, views, and stored procedures related to the model. Implementing custom Python scripts using Snowflake's Python API (Snowpark) to automatically apply tags during model deployment and retraining based on predefined rules and data quality checks.

5. You are using Snowpark Pandas to prepare data for a machine learning model. You have a Snowpark DataFrame named 'transactions df that contains transaction data, including 'transaction id', 'product id', 'customer id', and 'transaction_amount'. You want to create a new feature that represents the average transaction amount per customer. However, you are concerned about potential skewness in the 'transaction_amount' and want to apply a log transformation to reduce its impact before calculating the average. Which of the following steps using Snowpark Pandas would achieve this transformation and calculation most efficiently within Snowflake?

A) Option D
B) Option E
C) Option C
D) Option A
E) Option B

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