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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. Consider the following Python UDF intended to train a simple linear regression model using scikit-learn within Snowflake. The UDF takes feature columns and a target column as input and returns the model's coefficients and intercept as a JSON string. You are encountering an error during the CREATE OR REPLACE FUNCTION statement because of the incorrect deployment of the package during runtime. What would be the right way to fix this deployment and execute your model?

A) The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.
B) The required packages 'scikit-learn' is not present. The correct way to create UDF is by including the import statement within the function along with the deployment.
C) The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.
D) The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.
E) The code works seamlessly without modification as Snowflake automatically resolves all the dependencies and ensures the execution of code within the create or replace function statement.

2. You are building a time-series forecasting model in Snowflake to predict the hourly energy consumption of a building. You have historical data with timestamps and corresponding energy consumption values. You've noticed significant daily seasonality and a weaker weekly seasonality. Which of the following techniques or approaches would be most appropriate for capturing both seasonality patterns within a supervised learning framework using Snowflake?

A) Using Fourier terms (sine and cosine waves) with frequencies corresponding to daily and weekly cycles as features in a regression model.
B) Applying exponential smoothing directly to the original time series without feature engineering.
C) Decomposing the time series using STL (Seasonal-Trend decomposition using Loess) and building separate models for the trend and seasonal components, then combining the predictions.
D) Using a simple moving average to smooth the data before applying a linear regression model.
E) Creating lagged features (e.g., energy consumption from the previous hour, the same hour yesterday, and the same hour last week) and using these features as input to a regression model (e.g., Random Forest or Gradient Boosting).

3. You are building a model to predict loan defaults using data stored in Snowflake. As part of your feature engineering process within a Snowflake Notebook, you need to handle missing values in several columns: 'annual _ income', and You want to use a combination of imputation strategies: replace missing values with the median, 'annual_income' with the mean, and with a constant value of 0.5. You are leveraging the Snowpark DataFrame API. Which of the following code snippets correctly implements this imputation strategy?

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

4. You are building a machine learning model using Snowpark Python to predict house prices. The dataset contains a feature column named 'location' which contains free-form text descriptions of house locations. You want to leverage a pre-trained Large Language Model (LLM) hosted externally to extract structured location features like city, state, and zip code from the free-form text within Snowpark. You want to minimize the data transferred out of Snowflake. Which approach is most efficient and secure?

A) Use to load the 'location' column data into a Pandas DataFrame, call the external LLM API in your Python script to enrich the location data and then use to store the enriched data back into a Snowflake table.
B) Create a Snowpark User-Defined Function (UDF) that calls the external LLM API. Pass the 'location' column data to the UDF and retrieve the structured location features. Then apply the UDF directly on the Snowpark DataFrame.
C) Use the Snowflake Connector for Python to directly query the 'location' column and call the external LLM API from the connector. Then write the updated data into a new table.
D) Use Snowpark's 'createOrReplaceStage' to create an external stage pointing to the LLM API endpoint. Load the 'location' data into this stage and call the LLM API directly from the Snowflake stage using SQL.
E) Create a Snowflake External Function that calls the external LLM API. Pass the 'location' column data to the External Function and retrieve the structured location features. Then apply the External Function directly on the Snowpark DataFrame.

5. You are deploying a large language model (LLM) to Snowflake using a user-defined function (UDF). The LLM's model file, '11m model.pt', is quite large (5GB). You've staged the file to Which of the following strategies should you employ to ensure successful deployment and efficient inference within Snowflake? Select all that apply.

A) Split the large model file into smaller chunks and stage each chunk separately. Reassemble the model within the UDF code before inference.
B) Use the 'IMPORTS' clause in the UDF definition to reference Ensure the UDF code loads the model lazily (i.e., only when it's first needed) to minimize startup time and memory usage.
C) Increase the warehouse size to XLARGE or larger to provide sufficient memory for loading the large model into the UDF environment.
D) Leverage Snowflake's Snowpark Container Services to deploy the LLM as a separate containerized application and expose it via a Snowpark API. Then call that endpoint from snowflake.
E) Use the 'PUT' command with to compress the model file before staging it. Snowflake will automatically decompress it during UDF execution.

Solutions: