SPS-C01 : Snowflake Certified SnowPro Specialty - Snowpark

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Snowflake Certified SnowPro Specialty - Snowpark 認定 SPS-C01 試験問題:

1. You are developing a Snowpark Python application that performs advanced machine learning model training on a large dataset stored in Snowflake. You observe that the application is memory-intensive, causing frequent spilling to disk and slowing down the training process.
Which of the following strategies, when implemented in conjunction, is MOST likely to improve the performance of your Snowpark application in this memory-constrained scenario?

A) Reduce the batch size for data loading, utilize Snowpark's optimized data types for columns with small value ranges, and use memory profiling tools to identify memory leaks in your Python code and optimize the application.
B) Increase the 'MAX CONCURRENCY LEVEL' parameter at the session level and rewrite the model training code in SQL using stored procedures.
C) Switch to a larger Snowflake warehouse size and increase the parameter to ' True'.
D) Enable caching for intermediate results and switch to a Snowpark-optimized warehouse with a smaller size to force early spilling to disk.
E) Increase the size of the Snowpark-optimized warehouse with 'MEMORY OPTIMIZED server type, use appropriate data types to minimize memory footprint, and optimize UDFs to minimize intermediate data creation.

2. Consider the following Snowpark code snippet:

Which of the following statements are TRUE regarding the execution and performance of this code?

A) The 'filter' operation Ccol('column_a') > 100') will be executed only once because 'cached_df stores the materialized result.
B) The 'filter' operation Ccol('column_a') > 100') will be executed twice.
C) Removing 'cached_df = line would significantly improve the overall performance because caching always adds overhead.
D) The 'countl' operation will trigger the materialization and caching of 'filtered_df.
E) The 'count? operation will use the cached results of and apply an additional filter on the cached data.

3. You have two Snowpark DataFrames, 'dfl' and 'df2, representing customer data'. 'dfl' contains columns 'CUSTOMER ID', 'NAME, and 'EMAIL', while 'df2 contains 'CUSTOMER ID' and 'PURCHASE AMOUNT'. You need to create a new DataFrame that combines the information from both DataFrames but only includes customers who exist in BOTH 'dfl ' and 'df2 and the resulting DataFrame should have columns from both. Which of the following Snowpark DataFrame operations should you use, and what is the correct way to call it?

A)

B)

C)

D)

E)

4. You are developing a Snowpark application that processes large datasets stored in Snowflake. You need to implement custom User-Defined Functions (UDFs) written in Java. The UDF requires specific third-party libraries that are not available in the default Snowflake Java environment. What steps are necessary to package and deploy these UDFs correctly?

A) Utilize the Snowpark API to create a 'snowpark.functions.udf object, including the Java code and dependencies. The Snowpark runtime will handle the deployment.
B) Compile the Java code into a native library (e.g., a .so file), upload it to a Snowflake stage, and use the 'CREATE EXTERNAL FUNCTIONS command to invoke it.
C) Package the Java code and dependencies into a JAR file and upload it to a Snowflake stage. Use the 'CREATE FUNCTION' command with the 'IMPORTS' clause to reference the JAR file.
D) Create a separate Python UDF that imports the Java code using Jpype and then register the Python UDF with Snowflake
E) Use the 'CREATE OR REPLACE JAVA FUNCTION' command directly in Snowsight and paste the Java code along with the dependencies' contents into the function body.

5. Consider the following Snowpark Python code snippet designed to perform a join operation between two large tables: 'transactions' and 'customers'. The 'transactions' table contains billions of rows and the 'customers' table contains millions of rows. You are experiencing performance bottlenecks during the join operation. The 'transactions' table has a 'customer id' column that references the 'customers' table's primary key 'id'. Which optimization techniques would be MOST effective in improving the join performance within a Snowpark- optimized warehouse?

A) Increase the size of the Snowpark-optimized warehouse to provide more memory and CPU resources for the join operation.
B) Explicitly specify the join type as a broadcast join using a hint: 'df_transactions.join(df_customers, df_transactions['customer_id'] == df_customers['id'],
C) Repartition the 'transactions DataFrame by the 'customer_id' column and the 'customers' DataFrame by the 'id' column before performing the join using 'df_transactions.repartition('customer_id').join(df_customers.repartition('id'), df_transactions['customer_id'] == df_customers['id'])'.
D) Ensure that the 'customer_id' column in the 'transactions' table and the 'id' column in the 'customers' table have appropriate indexes defined in Snowflake before performing the join.
E) Broadcast the smaller DataFrame (customers') to all nodes in the warehouse before performing the join. Use: 'from snowflake.snowpark.functions import broadcast; df_transactions.join(broadcast(df_customers), df_transactions['customer_id']

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