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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You have deployed a vectorized Python UDF in Snowflake to perform sentiment analysis on customer reviews. The UDF uses a pre-trained transformer model loaded from a Stage. The model consumes a significant amount of memory (e.g., 5GB). Users are reporting intermittent 'Out of Memory' errors when calling the UDF, especially during peak usage. Which of the following strategies, used IN COMBINATION, would MOST effectively mitigate these errors and optimize resource utilization?

A) Implement lazy loading of the model within the UDF, ensuring it's only loaded once per warehouse node and reused across multiple invocations within that node.
B) Increase the warehouse size to provide more memory per node.
C) Increase the value of 'MAX BATCH_ROWS' for the UDF to process larger batches of data at once.
D) Partition the input data into smaller chunks using SQL queries and call the UDF on each partition separately.
E) Reduce the value of 'MAX for the UDF to process smaller batches of data.

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

3. You've developed a binary classification model using Snowpark ML to predict customer subscription renewal (0 for churn, 1 for renew). You want to visualize feature importance using a permutation importance technique calculated within Snowflake. You perform feature permutation and calculate the decrease in model performance (e.g., AUC) after each permutation. Suppose the following query represents the results of this process:

The 'feature_importance_results' table contains the following data:

Based on this output, which of the following statements are the MOST accurate interpretations regarding feature impact and model behavior?

A) The 'contract_length' and 'monthly_charges' features are equally important.
B) The 'contract_length' feature is the most important feature for the model's predictive performance; shuffling it causes the largest drop in AUC.
C) The 'support_calls' feature is the least important feature; removing it entirely from the model will have little impact on its AUC performance.
D) Permutation importance only reveals the importance of features within the current model. Different models trained with different features or algorithms might have different feature rankings.
E) Increasing the 'contract_length' for customers will always lead to a higher probability of renewal. However, there could be correlation between contract length and monthly charges.

4. You are troubleshooting an external function in Snowflake that calls a model hosted on Google Cloud A1 Platform. The external function consistently returns 'SQL compilation error: External function error: HTTP 400 Bad Request'. You have verified the API integration is correctly configured, and the Google Cloud project has the necessary permissions. Which of the following is the most likely cause of this error, and how would you best diagnose it?

A) The API integration in Snowflake is missing the necessary authentication credentials for Google Cloud. Diagnose by re-creating the API integration and ensuring the correct service account and scopes are configured.
B) The Google Cloud AI Platform model is unavailable or experiencing issues. Diagnose by checking the Google Cloud status dashboard for AI Platform outages.
C) The issue is most likely due to incorrect data types being passed from Snowflake to the Google Cloud A1 Platform model. Diagnose by examining the input data being sent to the function and comparing it to the model's expected input schema.
D) The request payload being sent by Snowflake exceeds the maximum size limit allowed by Google Cloud AI Platform. Diagnose by reducing the size of the input data and testing again.
E) There is a mismatch between the request headers sent by Snowflake and what the Google Cloud AI Platform endpoint expects, specifically the 'Content-Type'. Diagnose by examining the headers being sent by Snowflake and ensuring they match the expected format.

5. You are building a machine learning model to predict loan defaults. You have a dataset in Snowflake with the following features: 'income' (annual income in USD), 'loan_amount' (loan amount in USD), and 'credit_score' (FICO score). You need to normalize these features before training your model. The data has outliers in both 'income' and 'loan_amount', and 'credit_score' has a roughly normal distribution but you still want to standardize it to have a mean of 0 and standard deviation of 1. You want to perform these normalizations using only SQL in Snowflake (no UDFs). Which of the following SQL transformations are most suitable?

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

Solutions: