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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. 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) 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.
E) 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.

2. A data scientist is developing a Snowpark application that needs to authenticate to Snowflake using Key Pair Authentication. Which of the following steps are essential for configuring the Snowflake CLI to enable Key Pair Authentication and then correctly create a Snowpark session? (Select TWO)

A) Set the 'AUTHENTICATOR parameter in the Snowflake CLI configuration to 'EXTERNALBROWSER.
B) Generate an RSA key pair using 'ssh-keygen' and store the private key securely.
C) Set the 'AUTHENTICATOR parameter in the Snowflake CLI configuration to 'snowflake'.
D) Configure the user in Snowflake to use the public key by executing 'ALTER USER SET RSA_PUBLIC_KEY="'.
E) Set the 'PRIVATE KEY parameter in the Snowflake CLI configuration to the path of the private key file.

3. You have a SQL query stored in a file named 'query.sqr which contains several complex analytical calculations. The query depends on a Snowpark 'session' object already established. You want to create a Snowpark DataFrame from the result of this query. Which of the following code snippets achieves this with optimal performance and readability, assuming correct file access permissions?

A)

B)

C)

D)

E)

4. A Snowpark application needs to manage multiple sessions concurrently, each connected to a different Snowflake warehouse for resource isolation. The application receives warehouse names dynamically at runtime. How can the application efficiently create and manage these sessions while ensuring proper resource cleanup?

A) Create a global session object and use 'session.use_warehouse(warehouse_name)' before each operation. This implicitly switches the warehouse for all operations on that session.
B) Create a separate session object for each warehouse using a loop and store them in a list. Ensure each session is closed using 'session.close()' in a 'finally' block after use or using a context manager.
C) Create separate sessions for each warehouse, but only close the last session created to avoid disconnecting all connections.
D) Create a single session and create multiple Snowpark DataFrames, specifying the warehouse in each DataFrame's 'createOrReplaceTempView' method.
E) Create a single session object with connection pooling enabled and let Snowpark handle warehouse switching automatically based on the queries executed.

5. A data engineer has developed a Snowpark Python stored procedure, 'calculate daily summary', that processes sales data and generates a daily summary table. The procedure takes a date string as input and writes the summary to a table named 'DAILY SALES SUMMARY'. The engineer needs to operationalize this stored procedure to run automatically every day at midnight. Which of the following approaches is the MOST efficient and reliable way to schedule and execute this Snowpark stored procedure in Snowflake?

A) Use the Snowflake web interface to manually execute the stored procedure every day at midnight.
B) Create a Python script outside of Snowflake that uses the Snowflake Connector for Python to connect to Snowflake and execute the stored procedure using and schedule it using a system-level scheduler (e.g., cron or Windows Task Scheduler).
C) Develop a Snowpark UDF that wraps the stored procedure call and create a scheduled job in a third-party orchestration tool (e.g., Airflow, Prefect) to call the UDF.
D) Create a Snowflake Task that calls the stored procedure with a cron schedule that triggers the task every day at midnight. Ensure the task has a warehouse associated with it.
E) Use a Snowpipe to trigger the stored procedure when new data arrives in the staging area.

Solutions: