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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are developing a Snowpark Python application that connects to Snowflake using key pair authentication. You have the private key stored securely in an environment variable named 'SNOWFLAKE PRIVATE KEY. Which of the following code snippets correctly establishes a Snowpark session using this method, assuming all other necessary connection parameters (account, user, database, schema, warehouse) are also set as environment variables?

A)

B)

C)

D)

E)

2. A Snowpark application connects to Snowflake using key pair authentication. After several successful executions, the application starts failing with authentication errors. You suspect an issue with the private key. Considering best practices for security and troubleshooting, which of the following actions should you take FIRST to diagnose and resolve the problem?

A) Restart the Snowpark application server to refresh the session and clear any cached credentials.
B) Check the Snowflake login history in ACCOUNT _ USAGE view to identify the specific error message and the IP address from which the failed login attempts originated. Use new authtype and password.
C) Rotate the public/private key pair immediately and update the Snowpark application with the new private key. Generate new username and password.
D) Verify that the private key file exists at the specified path in the Snowpark application configuration and that the user running the application has read permissions on the file.
E) Immediately revoke the user's access to Snowflake and create a new user with a different private key.

3. You have a Snowpark Python application that performs several data transformations on a DataFrame representing customer transactions. The application is experiencing performance issues, and you suspect that some transformations are unnecessarily expensive. Which of the following techniques can MOST effectively optimize the performance of your Snowpark application, specifically focusing on minimizing data movement and leveraging Snowflake's query optimization capabilities?

A) Explicitly call .cache()' on the DataFrame after each transformation to materialize intermediate results in memory.
B) Use User-Defined Functions (UDFs) written in Python for all transformations, regardless of their complexity.
C) Take the dataframe to Pandas dataframe as soon as possible in between transformations, since Pandas dataframes will be faster.
D) Always use the largest available Snowflake warehouse size to ensure sufficient compute resources.
E) Leverage Snowpark's built-in DataFrame transformations (e.g., .groupBy()') to allow Snowflake to optimize the query execution plan. Avoid pulling large amounts of data into the client application for simple operations. Only call 'collect()' as the last and final option, as this is the most costly activity of all.

4. You are working with a Snowpark DataFrame containing website traffic data'. The DataFrame has columns like 'date' , 'page_url', and 'visit_count'. You need to calculate the cumulative sum of visit counts for each 'page_url' over time (i.e., ordered by 'date'). However, you only want to consider data from the last 30 days for each calculation. Which of the following Snowpark code snippets will correctly achieve this using window functions with a frame specification?

A)

B)

C)

D)

E)

5. You are using Snowpark Python to process a large dataset. You need to persist a DataFrame to a Snowflake table but want to ensure the operation is as efficient as possible and minimizes the data transfer overhead. The table already exists with the appropriate schema. Which of the following strategies would be the MOST efficient way to write the DataFrame to the existing table?

A) Write the DataFrame to a stage location as Parquet files and then use a 'COPY INTO existing_table FROM command.
B) Use 'df.write.insertlnto('existing_table')'.
C) Use with the default save mode (which is 'append').
D) Create a temporary table, load the DataFrame into it using , and then use a 'CREATE OR REPLACE TABLE AS SELECT FROM temp_table' statement.
E) Use 'df.write.mode('overwrite').saveAsTable('existing_table'Y.

Solutions: