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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You have deployed a regression model in Snowflake as an external function using AWS Lambda'. The external function takes several numerical features as input and returns a predicted value. You want to continuously monitor the model's performance in production and automatically retrain it when the performance degrades below a predefined threshold. Which of the following methods represent VALID approaches for calculating and monitoring model performance within the Snowflake environment and triggering the retraining process?

A) Build a Snowpark Python application deployed on Snowflake which periodically polls the external function's performance by querying the function with a sample data set and comparing results to ground truth stored in Snowflake. Initiate retraining directly from the Snowpark application if performance degrades.
B) Create a view that joins the input features with the predicted output and the actual result. Configure model monitoring within the AWS Sagemaker to perform continuous validation of the model.
C) Create a Snowflake Task that periodically executes a SQL query to calculate performance metrics (e.g., RMSE) by comparing predicted values from the external function with actual values stored in a separate table. Trigger a Python UDF, deployed as a Snowflake stored procedure, to initiate retraining if the RMSE exceeds the threshold.
D) Utilize Snowflake's Alerting feature, setting an alert rule based on the output of a SQL query that calculates performance metrics. Configure the alert action to invoke a webhook that triggers a retraining pipeline.
E) Implement custom logging within the AWS Lambda function to capture prediction results and actual values. Configure AWS CloudWatch to monitor these logs and trigger an AWS Step Function that initiates a new training job and updates the Snowflake external function with the new model endpoint upon completion.

2. You are tasked with building a machine learning pipeline in Snowpark Python to predict customer lifetime value (CLTV). You need to access and manipulate data residing in multiple Snowflake tables and views, including customer demographics, purchase history, and website activity. To improve code readability and maintainability, you decide to encapsulate data access and transformation logic within a Snowpark Stored Procedure. Given the following Python code snippet representing a simplified version of your stored procedure:

A) The 'session.write_pandas(df, table_name='CLTV PREDICTIONS', auto_create_table=Truey function writes the Pandas DataFrame 'df containing the CLTV predictions directly to a new Snowflake table named , automatically creating the table if it does not exist.
B) The 'session.sql('SELECT FROM PURCHASE line executes a SQL query against the Snowflake database and returns the results as a list of Row objects.
C) The replace=True, packages=['snowflake-snowpark-python', 'pandas', decorator registers the Python function as a Snowpark Stored Procedure, allowing it to be called from SQL.
D) The 'session.table('CUSTOMER DEMOGRAPHICS')' method creates a local Pandas DataFrame containing a copy of the data from the 'CUSTOMER DEMOGRAPHICS' table.
E) The 'snowflake.snowpark.context.get_active_session()' function retrieves the active Snowpark session object, enabling interaction with the Snowflake database from within the stored procedure.

3. You are tasked with predicting the sales price of houses based on their size (square footage) using linear regression in Snowflake. You have a table named 'HOUSE PRICES' with columns 'SQUARE FOOTAGE' and 'SALES PRICE'. You want to calculate the slope and intercept using Snowflake SQL. Which of the following queries, considering potential NULL values in the data, is the MOST robust and statistically sound for calculating the slope and intercept for a simple linear regression model?

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

4. A data scientist is tasked with creating features for a machine learning model predicting customer churn. They have access to the following data in a Snowflake table named 'CUSTOMER ID, 'DATE, 'ACTIVITY _ TYPE' (e.g., 'login', 'purchase', 'support_ticket'), and 'ACTIVITY VALUE (e.g., amount spent, duration of login). Which of the following feature engineering strategies, leveraging Snowflake's capabilities, could be useful for predicting customer churn? (Select all that apply)

A) Create features that capture the trend of customer activity over time (e.g., increasing or decreasing activity) using LACY and 'LEAD' window functions.
B) Use 'APPROX COUNT DISTINCT to estimate the number of unique product categories purchased by each customer within the last 3 months to create a features.
C) Calculate the recency, frequency, and monetary value (RFM) for each customer using window functions and aggregate functions.
D) Directly use the ACTIVITY TYPE column as a categorical feature without any transformation or engineering.
E) Create a feature representing the number of days since the customer's last login using "DATEDIFF and window functions.

5. You are working with a large dataset of sensor readings stored in a Snowflake table. You need to perform several complex feature engineering steps, including calculating rolling statistics (e.g., moving average) over a time window for each sensor. You want to use Snowpark Pandas for this task. However, the dataset is too large to fit into the memory of a single Snowpark Pandas worker. How can you efficiently perform the rolling statistics calculation without exceeding memory limits? Select all options that apply.

A) Explore using Snowpark's Pandas user-defined functions (UDFs) with vectorization to apply custom rolling statistics logic directly within Snowflake. UDFs allow you to use Pandas within Snowflake without needing to bring the entire dataset client-side.
B) Increase the memory allocation for the Snowpark Pandas worker nodes to accommodate the entire dataset.
C) Utilize the 'window' function in Snowpark SQL to define a window specification for each sensor and calculate the rolling statistics using SQL aggregate functions within Snowflake. Leverage Snowpark to consume the results of the SQL transformation.
D) Use the 'grouped' method in Snowpark DataFrame to group the data by sensor ID, then download each group as a Pandas DataFrame to the client and perform the rolling statistics calculation locally. Then upload back to Snowflake.
E) Break the Snowpark DataFrame into smaller chunks using 'sample' and 'unionAll', process each chunk with Snowpark Pandas, and then combine the results.

Solutions: