Free Snowflake SPS-C01 Exam Dumps Questions & Answers

You have a Snowpark DataFrame containing customer transaction data'. Your goal is to save this DataFrame as a set of Parquet files in an existing Snowflake stage named , partitioned by the 'transaction_date' column. You want to ensure that the files are automatically compressed using the Zstandard codec and that existing files with the same name are overwritten. Which of the following Snowpark code snippet achieves this with the most optimal approach and respects best practices?

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

You are developing a Snowpark Python application that processes streaming data using a dynamic table. The application is experiencing frequent 'net.snowflake.client.jdbc.SnowflakeSQLException: SQL compilation error: Unsupported feature 'Streaming Dynamic Table'. ' errors, even though dynamic tables are enabled in your Snowflake account and the user has the necessary privileges. Which of the following are potential causes and solutions for this error? (Select TWO)

• A.  The warehouse being used for the Snowpark session is not configured with the feature enabled. Verify that the warehouse configuration includes = TRUE'.
• B.  The dynamic table materialization schedule is too frequent, overwhelming the Snowflake warehouse. Increase the 'WAREHOUSE_SIZE' parameter of the dynamic table definition.
• C.  The user role lacks the 'EXECUTE MANAGED TASK privilege. Grant this privilege to the user role executing the Snowpark application.
• D.  The dynamic table definition contains unsupported SQL syntax, such as 'QUALIFY with complex window functions. Rewrite the dynamic table definition to use standard SQL constructs.
• E.  The Snowpark Python client version is outdated and does not support streaming dynamic tables. Upgrade to the latest version of the 'snowflake-snowpark- python' package.

You have two Snowflake tables, 'customers' and 'orders'. The 'customers' table contains customer information, including a 'customer id' and 'region'. The 'orders' table contains order information, including 'order id', 'customer id', and 'order amount'. You need to create a Snowpark DataFrame that joins these two tables on 'customer id' and calculates the total order amount per region. However, some customers may not have any orders, and you want to include all customers in the result, with a total order amount of 0 for those without orders. Which of the following Snowpark code snippets will achieve this goal MOST efficiently, assuming 'customers_df and 'orders_ff are pre-existing Snowpark DataFrames representing the respective tables?

• A. 
• B. 
• C. 
• D. 
• E. 

You are tasked with optimizing a Snowpark Python application that performs complex geospatial calculations on a large dataset. The application experiences significant performance bottlenecks due to the computational intensity of the geospatial functions. Which of the following strategies would be MOST effective in improving performance?

• A.  Rewrite the geospatial functions using native Python libraries within the Snowpark environment.
• B.  Increase the size of the virtual warehouse to a larger instance (e.g., from X-SMALL to LARGE).
• C.  Distribute the dataset into smaller chunks using partitioning strategies within the Snowpark DataFrame API and process them independently.
• D.  Disable automatic query optimization features in Snowflake to gain more control over query execution.
• E.  Utilize user-defined functions (UDFs) written in Java or Scala and leverage vectorized UDFs where possible.

You have a Snowpark application that utilizes a vectorized Python UDF to perform complex calculations on a large dataset. You notice that the performance is still not optimal. You suspect that the bottleneck might be related to how the data is being partitioned and processed by Snowflake. Which of the following actions, when performed in conjunction with vectorization, would MOST likely improve performance?

• A.  Increase the number of UDF worker threads within the UDF definition.
• B.  Repartition the Snowpark DataFrame using to align the data distribution with the computational needs of the UDF.
• C.  Broadcast the DataFrame to all compute nodes before applying the UDF.
• D.  Convert the DataFrame to a Pandas DataFrame before applying the UDF.
• E.  Ensure that the data is pre-sorted according to the primary key of the table before applying the UDF.