Snowflake Certified SnowPro Specialty - Snowpark - SPS-C01

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Snowflake SPS-C01 試験シラバストピック:

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

問題 #1

You are working with a Snowpark application designed to process data from an event table. While testing a complex transformation involving several joins and window functions, you encounter the following error: 'java.lang.OutOfMemoryError: Java heap space'. The application uses Snowpark DataFrames and is running on a reasonably sized virtual warehouse. What is the MOST likely cause of this error in the context of Snowpark and Snowflake?

A. Snowflake's internal query optimizer has generated a suboptimal execution plan, leading to excessive intermediate data materialization.
B. The virtual warehouse is undersized for the volume of data being processed, leading to excessive spilling to disk and eventual memory exhaustion on the driver node.
C. An inefficient UDF (User-Defined Function) is consuming excessive memory within the Java runtime.
D. The Snowpark driver process is attempting to load the entire result set into memory, exceeding the available heap space.
E. There's a circular dependency in the DataFrame transformations, causing an infinite loop and memory leak.

問題 #2

You are developing a Snowpark Python application that processes streaming data from an external source. The application requires near real-time insights and involves complex data transformations. However, you are observing high latency in the data processing pipeline. Which of the following optimization techniques would be MOST relevant to address this issue in the context of Snowpark and Snowflake?

A. Utilize Snowflake Streams and Tasks to incrementally transform and process the data within Snowflake, leveraging Snowpark UDFs for complex calculations.
B. Write the streaming data directly to external stages and query it using Snowpark DataFrames with external table access.
C. Implement Snowpipe to continuously load the streaming data into Snowflake tables and use Snowpark DataFrames to process the data incrementally.
D. Increase the virtual warehouse size to an extremely large instance to handle the high volume of streaming data.
E. Pre-aggregate the streaming data using an external stream processing engine (e.g., Apache Kafka, Apache Flink) before loading it into Snowflake.

問題 #3

You are tasked with building a Snowpark function to perform an upsert operation on a Snowflake table using a DataFrame. The function should take the target table name, a staging DataFrame, a join key column, and a list of columns to update. The function needs to handle potential schema evolution (i.e., columns may be added or removed from either the target table or the staging DataFrame) gracefully without causing the entire upsert to fail. Which of the following approaches, or combinations of approaches, would best address this requirement?

A. Rely on Snowflake's automatic schema detection during the 'merge' operation to automatically adapt to schema changes.
B. Before the merge, create a temporary table with the exact schema of the target table, insert all the data from the DataFrame into it, and then use the temporary table as source for the merge. Handle the schema evolution with dynamic sql if required.
C. Before the 'merge' operation, use 'DataFrame.select' on the staging DataFrame to project only the columns that exist in the target table.
D. Dynamically generate the SQL 'MERGE' statement within the function, comparing the columns present in the target table and the staging DataFrame, and only including those columns that exist in both.
E. Use the 'exceptAll' to ensure that there are no schema evolution issues.

問題 #4

You have a Snowpark DataFrame 'df with a column 'transaction_date' of STRING type, containing dates in 'YYYY-MM-DD' format, and a 'transaction_amount' column of STRING type, containing currency values like '$1 ,234.56'. You need to create a new DataFrame 'df_transformed' that contains the 'transaction_date' as a DATE type and 'transaction_amount' as a DOUBLE type. What Snowpark Python code snippet will accomplish this transformation most efficiently and handle potential errors during the cast?

A. ...python import snowflake.snowpark.functions as sf df_transformed = df.with_column('transaction_date', sf.to_date(sf.col('transaction_date'), format='YYYY- MM-DD')).with_column('transaction_amount', sf.to_double(sf.regexp_replace(sf.col('transaction_amount'), '[$,]', ")
B. ...python df_transformed = df.with_column('transaction_date', to_date(col('transaction_date') YYYY-MM-DD')).with_column('transaction_amount', col('transaction_amount').cast(DoubleType()))
C. ...python df_transformed = df.with_column('transaction_date', to_date(col('transaction_date'))).with_column('transaction_amount', to_double(col('transaction_amount').replace('$',").replace(',',")))
D. ...python from snowflake.snowpark.functions import try_to_date, try_to_double df_transformed = df.with_column('transaction_date', try_to_date(col('transaction_date'))).with_column('transaction_amount', try_to_double(regexp_replace(col('transaction_amount'), '[$,]', ")))
E. ...python df_transformed = df.select(to_date(col('transaction_date')).alias('transaction_date'), to_double(col('transaction_amount')).alias('transaction_amount'))

問題 #5

You are developing a Snowpark stored procedure in Python that needs to access and modify a temporary table within the same session.
Which of the following approaches is the MOST efficient and recommended way to achieve this?

A. Using the Python DB API (e.g., 'snowflake.connector' ) within the stored procedure to establish a separate connection to Snowflake and interact with the temporary table.
B. Using 'session.sql('CREATE TEMPORARY TABLE followed by subsequent 'session.sql('lNSERT INTO and 'session.sql('SELECT statements to interact with the temporary table.
C. Creating a global temporary table using 'CREATE GLOBAL TEMPORARY TABLE outside the stored procedure and then accessing it within the stored procedure using 'session.table()'.
D. Using 'session.createOrReplaceTempView()' to create a temporary view based on a DataFrame, and then querying the view using 'session.sql('SELECT
E. Using to create a DataFrame representing the temporary table, and then performing all operations using DataFrame transformations.

解説：