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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q246-Q251):

NEW QUESTION # 246
You are a data scientist working with a Snowflake table named 'CUSTOMER DATA' that contains a 'PHONE NUMBER' column stored as VARCHAR. The 'PHONE NUMBER' column sometimes contains non-numeric characters like hyphens and parentheses, and in some rows the data is missing. You need to create a new table 'CLEANED CUSTOMER DATA' with a column named 'CLEANED PHONE NUMBER that contains only the numeric part of the phone number (as VARCHAR) and replaces missing or invalid phone numbers with NULL. Which of the following Snowpark Python code snippets achieves this most efficiently, ensuring no errors occur during the data transformation, and considers Snowflake's performance best practices?

A. Option C
B. Option D
C. Option E
D. Option B
E. Option A

Answer: C

Explanation:
Option E is the most efficient because it leverages Snowpark's built-in functions for string manipulation and conditional logic directly. It first removes all non-numeric characters using 'regexp_replace' and then uses 'iff (if and only if) to replace empty strings (resulting from cleaning) with NULL. This approach avoids using UDFs (User-Defined Functions), which can introduce overhead. Option B, although using 'regexp_replace' , requires an additional 'with_column' to handle empty strings after cleaning. Option A introduces UDF that decreases performance. Option C calls UDF with undefined 'call_udf function and 'snowflake-snowpark-python' library. Option D is missing dataframe and its transformation is not happening on top of Dataframe. Option E is preferrable over Option B, as it uses the single transformation.

NEW QUESTION # 247
A retail company is using Snowflake to store transaction data'. They want to create a derived feature called 'customer _ recency' to represent the number of days since a customer's last purchase. The transactions table 'TRANSACTIONS has columns 'customer_id' (INT) and 'transaction_date' (DATE). Which of the following SQL queries is the MOST efficient and scalable way to derive this feature as a materialized view in Snowflake?

A. Option E
B. Option C
C. Option D
D. Option B
E. Option A

Answer: B

Explanation:
Option C is the most efficient because it correctly calculates the number of days since the last transaction using and 'DATEDIFF. The 'OR REPLACE clause ensures that the materialized view can be updated if it already exists. Options A and B are syntactically identical but A is slightly more correct since it considers the MAX. Option D calculates recency from the first transaction, which is incorrect. Option E is similar to option C but less performant since we want datediff on max(transaction_date) and not calculate and take the max over it.

NEW QUESTION # 248
You are deploying a machine learning model to Snowflake using a Python UDF. The model predicts customer churn based on a set of features. You need to handle missing values in the input data'. Which of the following methods is the MOST efficient and robust way to handle missing values within the UDF, assuming performance is critical and you don't want to modify the underlying data tables?

A. Use within the UDF, replacing missing values with a global constant (e.g., 0) defined outside the UDF. This constant is pre-calculated based on the training dataset's missing value distribution.
B. Pre-process the data in Snowflake using SQL queries to replace missing values with the mean for numerical features and the mode for categorical features before calling the UDF.
C. Use within the UDF to forward fill missing values. This assumes the data is ordered in a meaningful way, allowing for reasonable imputation.
D. Raise an exception within the UDF when a missing value is encountered, forcing the calling application to handle the missing values.
E. Implement a custom imputation strategy using 'numpy.where' within the UDF, basing the imputation value on a weighted average of other features in the row.

Answer: B

Explanation:
Pre-processing data in Snowflake with SQL for imputation offers several advantages. It allows leveraging Snowflake's compute resources for data preparation, rather than the UDF's limited resources. Handling missing values before the UDF call also simplifies the UDF code, making it more efficient and less prone to errors. Using 'fillna' within the UDF (options A, B, and C) can lead to performance bottlenecks and potential data leakage issues if not carefully managed. Raising an exception (option E) is not practical for production deployments where missing values are expected.

NEW QUESTION # 249
You are tasked with creating a new feature in a machine learning model for predicting customer lifetime value. You have access to a table called 'CUSTOMER ORDERS which contains order history for each customer. This table contains the following columns: 'CUSTOMER ID', 'ORDER DATE, and 'ORDER AMOUNT. To improve model performance and reduce the impact of outliers, you plan to bin the 'ORDER AMOUNT' column using quantiles. You decide to create 5 bins, effectively creating quintiles. You also want to create a derived feature indicating if the customer's latest order amount falls in the top quintile. Which of the following approaches, or combination of approaches, is most appropriate and efficient for achieving this in Snowflake? (Choose all that apply)

A. Use the window function to create quintiles for 'ORDER AMOUNT and then, in a separate query, check if the latest 'ORDER AMOUNT for each customer falls within the NTILE that represents the top quintile.
B. Create a temporary table storing quintile information, then join this table to original table to find the top quintile order amount.
C. Use a Snowflake UDF (User-Defined Function) written in Python or Java to calculate the quantiles and assign each 'ORDER AMOUNT to a bin. Later you can use other statement to check the top quintile amount from result set.
D. Use 'WIDTH_BUCKET function, after finding the boundaries of quantile using 'APPROX_PERCENTILE' or 'PERCENTILE_CONT. Using MAX(ORDER to determine recent amount is in top quantile.
E. Calculate the 20th, 40th, 60th, and 80th percentiles of the 'ORDER AMOUNT' using 'APPROX PERCENTILE or 'PERCENTILE CONT and then use a 'CASE statement to assign each order to a quantile bin. Calculate and see if on that particular date is in top quintile.

Answer: A,D,E

Explanation:
Options A, B, and E are valid and efficient approaches. Option A using 'NTILE' is a direct and efficient way to create quantile bins within Snowflake SQL, and can find the most recent order date for customer with a case statement. Option B calculates the percentiles directly and then uses a CASE statement to assign bins. This is also efficient for explicit boundaries. Option E finds the boundaries of the quantile using 'APPROX_PERCENTILE or 'PERCENTILE_CONT , after that you can use 'WIDTH_BUCKET to categorize into quantile bins based on ranges. Option C is possible but generally less efficient due to the overhead of UDF execution and data transfer between Snowflake and the UDF environment. Option D is valid, but creating a temporary table adds complexity and potentially reduces performance compared to window functions or direct quantile calculation within the query.

NEW QUESTION # 250
You are tasked with automating the retraining of a Snowpark ML model based on the performance metrics of the deployed model. You have a table 'MODEL PERFORMANCE that stores daily metrics like accuracy, precision, and recall. You want to automatically trigger retraining when the accuracy drops below a certain threshold (e.g., 0.8). Which of the following approaches using Snowflake features and Snowpark ML is the MOST robust and cost-effective way to implement this automated retraining pipeline?

A. Implement an external service (e.g., AWS Lambda or Azure Function) that periodically queries the "MODEL_PERFORMANCE table using the Snowflake Connector and triggers a Snowpark ML model training script via the Snowflake API.
B. Implement a Snowpark ML model training script that automatically retrains the model every day, regardless of the performance metrics. This script will overwrite the previous model.
C. Create a Dynamic Table that depends on the 'MODEL PERFORMANCE table and materializes when the accuracy is below the threshold. This Dynamic Table refresh triggers a Snowpark ML model training stored procedure. This stored procedure saves the new model with a timestamp and updates a metadata table with the model's details.
D. Use a Snowflake stream on the 'MODEL_PERFORMANCE table to detect changes in accuracy, and trigger a Snowpark ML model training function using a PIPE whenever the accuracy drops below the threshold.
E. Create a Snowflake task that runs every hour, queries the 'MODEL_PERFORMANCE table, and triggers a Snowpark ML model training script if the accuracy threshold is breached. The training script will overwrite the existing model.

Answer: C

Explanation:
Option D is the most robust and cost-effective solution. Using a Dynamic Table ensures that retraining is triggered only when necessary (when accuracy drops below the threshold). The Dynamic Table's materialization event then kicks off a Snowpark ML model training stored procedure that automatically retrains the model. This stored procedure saves the new model with a timestamp and updates a metadata table, allowing for version control. This eliminates unnecessary retraining runs (cost savings) and provides full lineage of models. Option A can be wasteful as it retrains even if it's not required. Option B using Stream & Pipes doesn't trigger model re-training after data accuracy breach. Option C doesn't account for model performance leading to unnecessary retrains. Option E introduces external dependencies and complexity that are best avoided within the Snowflake ecosystem.

NEW QUESTION # 251
......

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