Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are tasked with feature engineering a dataset containing customer transaction data stored in a Snowflake table named 'CUSTOMER TRANSACTIONS'. This table includes columns like 'CUSTOMER ID', 'TRANSACTION DATE, and 'TRANSACTION AMOUNT. You need to create a new feature representing the 'Recency' of the customer, which is the number of days since their last transaction. Using Snowpark Pandas, which of the following code snippets will correctly calculate the Recency feature as a new column in a Snowpark DataFrame?

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

2. You've built a customer churn prediction model in Snowflake, and are using the AUC as your primary performance metric. You notice that your model consistently performs well (AUC > 0.85) on your validation set but significantly worse (AUC < 0.7) in production. What are the possible reasons for this discrepancy? (Select all that apply)

A) The AUC metric is inherently unreliable and should not be used for model evaluation.
B) The production environment has significantly more missing data compared to the training and validation environments.
C) Your model is overfitting to the validation data. This causes to give high performance on validation set but less accurate in the real world.
D) There's a temporal bias: the customer behavior patterns have changed since the training data was collected.
E) Your training and validation sets are not representative of the real-world production data due to sampling bias.

3. 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)

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

4. 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 B
B) Option A
C) Option D
D) Option E
E) Option C

5. You are designing a feature engineering pipeline using Snowpark Feature Store for a fraud detection model. You have a transaction table in Snowflake. One crucial feature is the 'average_transaction_amount_last_7_days' for each customer. You want to implement this feature using Snowpark Python and materialize it in the Feature Store. You have the following Snowpark DataFrame 'transactions_df containing 'customer_id' and 'transaction_amount'. Which of the following code snippets correctly defines and registers this feature in the Snowpark Feature Store, ensuring efficient computation and storage?

A)

B)

C)

D)

E)

Solutions: