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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are tasked with identifying Personally Identifiable Information (PII) within a Snowflake table named 'customer data'. This table contains various columns, some of which may contain sensitive information like email addresses and phone numbers. You want to use Snowflake's data governance features to tag these columns appropriately. Which of the following approaches is the MOST effective and secure way to automatically identify and tag potential PII columns with the 'PII CLASSIFIED tag in your Snowflake environment, ensuring minimal manual intervention and optimal accuracy?

A) Create a custom Snowpark for Python UDF that uses regular expressions to analyze the data in each column and apply the 'PII_CLASSIFIED tag if a match is found. Schedule this UDF to run periodically using Snowflake Tasks.
B) Manually inspect each column in the 'customer_data' table and apply the 'PII_CLASSIFIED' tag to columns that appear to contain PII based on their names and a small sample of data.
C) Use Snowflake's built-in classification feature with a pre-defined sensitivity category to identify potential PII columns. Associate a masking policy that redacts the data, and apply a tag 'PII_CLASSIFIED' via automated tagging to the columns identified as containing PII.
D) Write a SQL script to query the 'INFORMATION SCHEMA.COLUMNS' view, identify columns with names containing keywords like 'email' or 'phone', and then apply the 'PII_CLASSIFIED tag to those columns.
E) Export the 'customer_data' to a staging area in cloud storage, use a third-party data discovery tool to scan for PII, and then manually apply the "PII_CLASSIFIED' tag to the corresponding columns in Snowflake based on the tool's findings.

2. You have trained a linear regression model in Snowpark ML to predict house prices. After training, you want to assess the overall feature importance using the model's coefficients. Consider the following Snowflake table containing the coefficients:

Which of the following statements are correct interpretations of these coefficients regarding feature impact?

A) The 'location_score' feature is the most influential predictor in determining house price.
B) The 'age' feature has an insignificant impact because its coefficient is small.
C) An increase of one square foot (sqft) in house size is associated with an increase of $120.5 in the predicted house price.
D) Increasing the number of bedrooms is associated with a decrease in the predicted house price.
E) The 'bedrooms' feature has a positive impact on the house price since the coefficient is negative.

3. A Snowflake table named 'SALES DATA contains a 'TRANSACTION DATE column stored as VARCHAR. The data in this column is inconsistent; some rows have dates in 'YYYY-MM-DD' format, others in 'MM/DD/YYYY' format, and some contain invalid date strings like 'N/A'. You need to standardize all dates to 'YYYY-MM-DD' format and store them in a new column called FORMATTED DATE in a new table 'STANDARDIZED_SALES DATA. Which of the following approaches, using Snowpark Python and SQL, most effectively handles these inconsistencies and minimizes errors during data transformation? Select all that apply:

A) Using a single 'TO_DATE function with format parameter set to 'AUTO' combined with 'TO_VARCHAR to format the date to 'YYYY-MM-DD'.
B) Using a Snowpark Python UDF to parse each date string individually, handling different formats with conditional logic, and returning a formatted date string. This provides flexibility in handling diverse date formats.
C) Employing Snowpark's error handling mechanism (e.g., 'try...except' blocks) within a loop to iteratively convert each date string, catching and logging errors, and storing valid dates in a new column.
D) Using a series of DATE" and 'TO_VARCHAR SQL functions in Snowpark to attempt converting the date in different formats and then formatting the result to 'YYYY-MM-DD'. Any conversion failing returns NULL.
E) Creating a view on top of 'SALES_DATA' that implements the conversion logic. This avoids creating a new physical table immediately and allows for experimentation with different conversion strategies before materializing the data.

4. You are tasked with estimating the 95% confidence interval for the median annual income of Snowflake customers. Due to the non-normal distribution of incomes and a relatively small sample size (n=50), you decide to use bootstrapping. You have a Snowflake table named 'customer_income' with a column 'annual_income'. Which of the following SQL code snippets, when correctly implemented within a Python script interacting with Snowflake, would most accurately achieve this using bootstrapping with 1000 resamples and properly calculate the confidence interval?

A)

B)

C)

D)

E)

5. You are tasked with building a fraud detection model using Snowflake and Snowpark Python. The model needs to identify fraudulent transactions in real-time with high precision, even if it means missing some actual fraud cases. Which combination of optimization metric and model tuning strategy would be most appropriate for this scenario, considering the importance of minimizing false positives (incorrectly flagging legitimate transactions as fraudulent)?

A) Log Loss, optimized with a grid search focusing on hyperparameters that improve overall accuracy.
B) F 1-Score, optimized to balance precision and recall equally.
C) AUC-ROC, optimized with a randomized search focusing on hyperparameters related to model complexity.
D) Precision, optimized with a threshold adjustment to minimize false positives.
E) Recall, optimized with a threshold adjustment to minimize false negatives.

Solutions: