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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You have trained a logistic regression model in Python using scikit-learn and plan to deploy it as a Python stored procedure in Snowflake. You need to serialize the model for deployment. Consider the following code snippet:

A) The code will fail because the 'model_bytes' variable is not accessible within the 'predict' function's scope.
B) The code will fail because Snowflake stages cannot be used to store model objects.
C)

D) The code will fail because it does not handle potential security vulnerabilities associated with deserializing pickled objects from untrusted sources.
E) The code will execute successfully. The model serialization and deserialization using pickle are correctly implemented within the stored procedure.

2. You have a Snowflake table 'PRODUCT_PRICES' with columns 'PRODUCT_ID' (INTEGER) and 'PRICE' (VARCHAR). The 'PRICE' column sometimes contains values like '10.50 USD', '20.00 EUR', or 'Invalid Price'. You need to convert the 'PRICE column to a NUMERIC(10,2) data type, removing currency symbols and handling invalid price strings by replacing them with NULL. Considering both data preparation and feature engineering, which combination of Snowpark SQL and Python code snippets achieves this accurately and efficiently, preparing the data for further analysis?

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

3. You are building a data science pipeline in Snowflake to predict customer churn. The pipeline includes a Python UDF that uses a pre- trained scikit-learn model stored as a binary file in a Snowflake stage. The UDF needs to load this model for prediction. You've encountered an issue where the UDF intermittently fails, seemingly related to resource limits when multiple concurrent queries invoke the UDF. Which of the following strategies would best optimize the UDF for concurrency and resource efficiency, minimizing the risk of failure?

A) Load the scikit-learn model inside the UDF function on every invocation to ensure the latest version is used.
B) Utilize Snowflake's session-level caching by storing the loaded model in 'session.get('model')' to be reused across multiple UDF calls within the same session. Reload the model if 'session.get('model')' is None.
C) Increase the memory allocated to the Snowflake warehouse to accommodate multiple UDF invocations.
D) Implement a global, lazy-loaded cache for the scikit-learn model within the UDF's module. The model is loaded only once during the first invocation and shared across subsequent calls. Protect the loading process with a lock to prevent race conditions in concurrent environments.
E) Load the scikit-learn model outside the UDF function in the global scope of the module so that all invocations share the same loaded model instance. Use the 'context.getExecutionContext(Y to track execution, making sure it is thread safe.

4. You are tasked with deploying a time series forecasting model within Snowflake using Snowpark Python. The model requires significant pre-processing and feature engineering steps that are computationally intensive. These steps include calculating rolling statistics, handling missing values with imputation, and applying various transformations. You aim to optimize the execution time of these pre- processing steps within the Snowpark environment. Which of the following techniques can significantly improve the performance of your data preparation pipeline?

A) Ensure that all data used is small enough to fit within the memory of the client machine running the Snowpark Python script, thus removing the need for distributed computing.
B) Force single-threaded execution by setting to avoid overhead associated with parallel processing.
C) Utilize Snowpark's vectorized UDFs and DataFrame operations to leverage Snowflake's distributed computing capabilities.
D) Convert the Snowpark DataFrame to a Pandas DataFrame using and perform all pre-processing operations using Pandas functions before loading the processed data back to Snowflake.
E) Write the feature engineering logic directly in SQL and create a view. Use the Snowpark DataFrame API to query the view, avoiding Python code execution within Snowpark.

5. You're building a regression model using Snowpark Python to predict house prices. After initial training, you observe that the model consistently overestimates the prices of high-value houses and underestimates the prices of low-value houses. Given the options below, which optimization metric, along with code snippet to calculate it using Snowpark, would be most effective in addressing this specific issue?

A) R-squared - as it measures the proportion of variance explained, directly addressing how well the model fits the data across all price ranges.

B) Mean Absolute Error MAE - as it is sensitive to outliers and will penalize large errors more heavily.

C) Root Mean Squared Error (RMSE) - as it gives more weight to larger errors, making it suitable for addressing the underestimation/overestimation problem.

D) Mean Squared Error (MSE) - as it is less sensitive to outliers than RMSE.

E) Adjusted R-squared - as it penalizes the addition of irrelevant features, improving the model's generalization ability.

Solutions: