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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have created a Snowpark stored procedure in Python that accesses a Snowflake stage to read configuration files. To enhance security, you want to grant the stored procedure specific permissions to only read files from that stage, without granting broader account- level access. Which of the following approaches is the MOST secure and granular way to achieve this?

A) Create a Snowflake custom role and grant the 'READ' privilege on the stage to this role, then grant this role to the stored procedure's owner.
B) Create a stored procedure with 'EXECUTE AS CALLER and ensure the caller (user executing the procedure) has the necessary privileges.
C) Grant the 'READ privilege directly to the stored procedure using the 'ALTER PROCEDURE command.
D) Grant the 'USAGE privilege on the stage to the role that owns the stored procedure.
E) Grant the 'SELECT privilege on the stage to the role that owns the stored procedure.

2. You are developing a Snowpark application in Python to perform sentiment analysis on customer reviews stored in a Snowflake table named 'CUSTOMER_REVIEWS. The table has columns 'REVIEW ONT), 'REVIEW TEXT (VARCHAR), and 'SENTIMENT SCORE (FLOAT). You want to define a UDF using Snowpark that leverages a pre-trained sentiment analysis model from the 'nltk' library (already uploaded to a stage). The UDF should take 'REVIEW TEXT' as input and return the sentiment score. Which of the following code snippets will correctly define and register the UDF, ensuring it's accessible for use in Snowpark DataFrames, taking into account potential serialization issues with 'nltk' models?

A)

B)

C)

D)

E)

3. You have a Snowpark DataFrame with columns 'order_id', 'product_id', 'sale_date' (DATE), and 'sale_amount'. You need to perform the following transformations: 1. Filter out sales records before January 1, 2023.2. Group the data by 'product_id' and calculate the total 'sale_amount' for each product. 3. Create a new column 'average_sale_amount' by dividing the total 'sale_amount' by the number of distinct 'order_id' for each product. You must alias the aggregate function. Which of the following Snowpark code snippets correctly implements these transformations?

A)

B)

C)

D)

E)

4. You are tasked with building a Snowpark function to perform an upsert operation on a Snowflake table using a DataFrame. The function should take the target table name, a staging DataFrame, a join key column, and a list of columns to update. The function needs to handle potential schema evolution (i.e., columns may be added or removed from either the target table or the staging DataFrame) gracefully without causing the entire upsert to fail. Which of the following approaches, or combinations of approaches, would best address this requirement?

A) Dynamically generate the SQL 'MERGE' statement within the function, comparing the columns present in the target table and the staging DataFrame, and only including those columns that exist in both.
B) Rely on Snowflake's automatic schema detection during the 'merge' operation to automatically adapt to schema changes.
C) Before the merge, create a temporary table with the exact schema of the target table, insert all the data from the DataFrame into it, and then use the temporary table as source for the merge. Handle the schema evolution with dynamic sql if required.
D) Use the 'exceptAll' to ensure that there are no schema evolution issues.
E) Before the 'merge' operation, use 'DataFrame.select' on the staging DataFrame to project only the columns that exist in the target table.

5. You are developing a Snowpark Python application that reads a large dataset (1 TB) from a Snowflake table 'TRANSACTIONS and performs complex aggregations. The application is experiencing significant performance issues, with query execution taking several hours. You have already verified that the warehouse size is appropriate and caching is enabled. You suspect the issue might be related to data skew and incorrect partitioning. Which of the following strategies would be MOST effective in identifying and mitigating this performance bottleneck?

A) Implement caching using after reading the data from the 'TRANSACTIONS' table and before performing any aggregations.
B) Use partition_expression=sf.rand())' to randomly repartition the DataFrame into 100 partitions, regardless of the data distribution in the ' TRANSACTIONS table.
C) Increase the Snowflake warehouse size to the largest available option (e.g., X6-Large) to provide more resources for query execution, without analyzing data distribution.
D) Analyze the 'TRANSACTIONS' table's data distribution using and histograms on the join keys. Based on the analysis, use with the most skewed column to redistribute the data more evenly. Also, consider using bucketing if appropriate.
E) Use to force a broadcast join, assuming the aggregated data is small enough to fit in memory. Monitor query profiles to confirm the broadcast occurs.

Solutions: