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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are building a predictive model for customer churn using linear regression in Snowflake. You have identified several features, including 'CUSTOMER AGE', 'MONTHLY SPEND', and 'NUM CALLS'. After performing an initial linear regression, you suspect that the relationship between 'CUSTOMER AGE and churn is not linear and that older customers might churn at a different rate than younger customers. You want to introduce a polynomial feature of "CUSTOMER AGE (specifically, 'CUSTOMER AGE SQUARED') to your regression model within Snowflake SQL before further analysis with python and Snowpark. How can you BEST create this new feature in a robust and maintainable way directly within Snowflake?

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

2. You're a data scientist analyzing sensor data from industrial equipment stored in a Snowflake table named 'SENSOR READINGS' The table includes 'TIMESTAMP' , 'SENSOR ID', 'TEMPERATURE', 'PRESSURE', and 'VIBRATION'. You need to identify malfunctioning sensors based on outlier readings in 'TEMPERATURE' , 'PRESSURE' , and 'VIBRATION'. You want to create a dashboard to visualize these outliers and present a business case to invest in predictive maintenance. Select ALL of the actions that are essential for both effectively identifying sensor outliers within Snowflake and visualizing the data for a business presentation. (Multiple Correct Answers)

A) Calculate Z-scores for 'TEMPERATURE, 'PRESSURE, and 'VIBRATION' for each 'SENSOR_ID within a rolling window of the last 24 hours using Snowflake's window functions. Define outliers as readings with Z-scores exceeding a threshold (e.g., 3).
B) Directly connect the 'SENSOR_READINGS' table to a visualization tool and create a 3D scatter plot with 'TEMPERATURE, 'PRESSURE, and 'VIBRATION' on the axes, without any pre-processing or outlier detection in Snowflake.
C) Implement a clustering algorithm (e.g., DBSCAN) within Snowflake using Snowpark Python to group similar sensor readings, identifying outliers as points that do not belong to any cluster or belong to very small clusters.
D) Create a Snowflake stored procedure to automatically flag outlier readings in a new column 'IS OUTLIER based on a predefined rule set (e.g., IQR method or Z-score threshold), and then use this column to filter data for visualization in a dashboard.
E) Calculate basic statistical summaries (mean, standard deviation, min, max) for each sensor and each variable C TEMPERATURE, 'PRESSURE, and 'VIBRATION') and use that information to filter down to the most important sensor, prior to using the other techniques.

3. A data scientist is developing a model within a Snowpark Python environment to predict customer churn. They have established a Snowflake session and loaded data into a Snowpark DataFrame named 'customer data'. The feature engineering pipeline requires a custom Python function, 'calculate engagement_score', to be applied to each row. This function takes several columns as input and returns a single score representing customer engagement. The data scientist wants to apply this function in parallel across the entire DataFrame using Snowpark's UDF capabilities. The following code snippet is used to define and register the UDF:

When the UDF is called the above error is observed. What change needs to be applied to make the UDF work as expected?

A) Redefine the function to accept string arguments and cast them to the correct data types within the function.
B) Add '@F.sproc' decorator before the function definition.
C) Remove argument from 'session.udf.register' call. Snowpark can infer the input types automatically.
D) Wrap the Python function inside a stored procedure using @F.sproc' and call that stored procedure instead of the plain python function.
E) Change the function call to use the Snowpark DataFrame's 'select' function with column objects: 'customer_data.select(engagement_score_udf(F.col('num_transactions'), F.col('avg_transaction_value'),

4. You are developing a real-time fraud detection system using Snowpark and deploying it as a Streamlit application connected to Snowflake. The system ingests transaction data continuously and applies a pre-trained machine learning model (stored as a binary file in Snowflake's internal stage) to score each transaction for fraud. You need to ensure the model loading process is efficient, and you're aiming to optimize performance by only loading the model once when the application starts, not for every single transaction. Which combination of approaches will BEST achieve this in a reliable and efficient manner, considering the Streamlit application's lifecycle and potential concurrency issues?

A) Use the 'st.cache_data' decorator in Streamlit to cache the loaded model and Snowpark session. Load the model directly from the stage within the cached function. This approach handles concurrency and ensures the model is only loaded once per session.
B) Use Python's built-in 'threading.Lock' to serialize access to the model loading code and the Snowpark session, preventing concurrent access from multiple Streamlit user sessions. Store the loaded model in a module-level variable.
C) Leverage the 'snowflake.snowpark.Session.read_file' to load the model binary directly into a Snowpark DataFrame and then convert to a Pandas DataFrame. Then, use the 'pickle' library for deserialization.
D) Load the model outside of the Streamlit application's execution context (e.g., in a separate script) and store it in a global variable. Access this global variable within the Streamlit application. This approach requires careful handling of concurrency.
E) Load the model within a try-except block and set the Snowpark session as a singleton that will guarantee model loads once for the entire application.

5. You've trained a model using Snowflake ML and want to deploy it for real-time predictions using a Snowflake UDF. To ensure minimal latency, you need to optimize the UDF's performance. Which of the following strategies and considerations are most important when creating and deploying a UDF for model inference in Snowflake to minimize latency, especially when the model is large (e.g., > 100MB)?
Select all that apply.

A) Ensure the UDF code is written in Python and utilizes vectorized operations with libraries like NumPy to process data in batches efficiently.
B) Utilize a Snowflake external function instead of a UDF if the model requires access to resources outside of Snowflake's environment.
C) Use a Snowflake Stage to store the model file and load the model within the UDF using 'snowflake.snowpark.files.SnowflakeFile' to minimize memory footprint.
D) Use smaller warehouse size for UDF evaluation in order to reduce latency and compute costs.
E) Store the trained model as a BLOB within the UDF code itself to avoid external dependencies.

Solutions: