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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark Python application that interacts with Snowflake using a service account. You are rotating the private key associated with the service account. After updating the private key in your application's configuration, you encounter an error during the connection attempt: 'SnowflakeSQLException: 390103 (OSAOO): Failed to connect to DB. Encountered exception while creating connection: Authentication token has expired.' What is the MOST likely cause of this error, and what steps should you take to resolve it?

A) The connection string contains invalid characters. Ensure the account identifier and other parameters are correctly specified.
B) The Snowflake cache still holds the old private key. Clear the Snowflake connection cache in the application by calling and restarting the application.
C) The public key associated with the new private key has not been authorized in Snowflake for the service account. Ensure that the public key is associated with the service account using ALTER SERVICE ACCOUNT SET RSA PUBLIC KEY =";'
D) The private key is in an incorrect format. Ensure that the private key is in PKCS#8 format and is properly encoded.
E) The Snowflake service account hasn't been granted sufficient permissions to access the required resources. Re-grant the necessary roles and privileges to the service account.

2. You're developing a Snowpark application that reads data from a Snowflake table, performs several transformations, and then writes the results back to a different table. You want to ensure that the entire process is executed as a single atomic transaction, even if it involves multiple Snowpark DataFrames and operations. Which of the following actions are required to achieve this transactional behavior?

A) Ensure that the target table for writing the results has the 'TRANSIENT' property set to 'TRUE'.
B) Explicitly start a transaction using 'session.beginTransaction()' at the beginning of the Snowpark application and commit it using 'session.commitTransaction(Y at the end.
C) Configure the Snowpark session with the parameter set to ' FALSE
D) Leverage the 'CREATE OR REPLACE TABLE AS SELECT statement within a Stored Procedure called from your Snowpark code. All DML operations done as part of stored proc is transactional
E) All Snowpark operations within a single session are automatically executed as a single atomic transaction by default; no additional configuration is required.

3. You have a Snowflake table named 'RAW EVENTS with a large number of events data, containing columns like 'EVENT ID', 'TIMESTAMP, 'USER ID, and 'EVENT_TYPE. The 'EVENT TYPE column contains string values representing different event categories. You want to create a Snowpark DataFrame, but due to the table's size, you only want to sample a small portion of the data for initial exploration and testing. Which of the following code snippets MOST accurately and efficiently creates a sampled Snowpark DataFrame named 'sampled_df containing approximately 1% of the rows from the 'RAW EVENTS table?

A)

B)

C)

D)

E)

4. You have a Snowpark DataFrame named and want to create a stored procedure that calculates the average purchase amount for each customer. The stored procedure should accept the DataFrame as input, perform the aggregation, and return a new DataFrame with the results. Which of the following code snippets BEST demonstrates how to correctly define and deploy this stored procedure?

A)

B)

C)

D)

E)

5. You are profiling a Snowpark application that uses a combination of SQL queries and Python UDFs. You observe that a particular stage involving a UDF is taking significantly longer than expected. You suspect that the UDF's performance is the bottleneck. Which of the following steps would be the MOST comprehensive approach to diagnose and address the performance issue?

A) Replace the Python UDF with an equivalent SQL query using Snowflake's built-in functions. If the SQL query performs better, the Python UDF was the bottleneck.
B) Increase the warehouse size and re-run the application. If the execution time improves significantly, the issue was resource contention.
C) Convert the scalar UDF to a vectorized UDF, even without fully understanding the source of the performance bottleneck.
D) Use Snowflake's query profile to examine the execution plan and identify the UDF-related stages with the highest execution time. Then, analyze the UDF's code for inefficiencies, such as unnecessary loops or complex calculations.
E) Implement caching for the UDF's results to avoid recomputing the same values multiple times.

Solutions: