Free Access Snowflake.SPS-C01.v2026-07-23.q130 Practice Test (Page 8)

Question 31

You're developing a Snowpark Python application to process log files stored in an external stage 's3_logs'. These logs are in plain text, with each line representing a log entry. You need to filter log entries based on a specific keyword and extract timestamps from the matching lines. Which of the following approaches, using and Snowpark DataFrames, will efficiently accomplish this, avoiding unnecessary data transfer to the client?

A.Download each log file using 'SnowflakeFile.get' , read the file line by line in the client application, filter lines based on the keyword, extract timestamps, and then create a Snowpark DataFrame with the extracted timestamps.

B.Use pattern=' .log')' to load all log files into a Snowpark DataFrame with a single column containing the log lines. Then, filter the DataFrame using like' to find lines containing the keyword and extract timestamps using regular expressions in a subsequent select statement.

C.Create a UDF that accepts a 'SnowflakeFile' object as input, reads the file line by line inside the UDF, filters lines based on the keyword, extracts timestamps, and returns a list of timestamps. Call this UDF on a Snowpark DataFrame created using 'session.read.option('PATTERN', ' .10g').format('CSV').load('@s3_logs'V.

D.Create an external function that accesses the files directly from S3, performs the filtering and timestamp extraction, and returns the results. Then create a Snowpark DataFrame to invoke the external function.

E.Use 'session.read.option('PATTERN', ' to load all log files into a Snowpark DataFrame with a single column containing the log lines. Create a UDTF that reads a partition of the DataFrame, filters lines based on the keyword, extracts timestamps, and returns a table of timestamps.

Question 32

You are developing a Snowpark application to ingest a large dataset into Snowflake. You have a DataFrame with a schema that matches the target table 'TARGET TABLE. Due to network constraints, you need to optimize the insertion process to minimize the number of API calls. Which of the following approaches would provide the MOST efficient way to insert the data?

A.Iterate through the rows of 'data_df , constructing and executing a separate INSERT statement for each row using 'session.sql()'.

B.Convert 'data_df to a Pandas DataFrame using and then use to insert the data.

C.Directly use the method, without any intermediate transformations.

D.Stage 'data_df to an internal stage, then load the data from stage to the table using COPY INTO command.

E.Load the DataFrame into chunks of 1000 records into target table by using insert_into() function iteratively

Question 33

You have a Snowpark DataFrame named 'orders df with columns 'order id', 'customer id', 'order date', and 'total amount'. You need to create a new DataFrame that contains only the 'customer_id" and the total number of orders placed by each customer. However, you want to perform this aggregation in parallel using a user-defined function (UDF) to improve performance. Which approach is MOST efficient and CORRECT?

A.Create a UDF that takes a customer ID as input and returns the count of orders for that customer, then apply this UDF to each distinct customer ID using 'map'.

B.Create a UDF that performs the entire aggregation and call it with 'orders_df. The UDF uses a Pandas DataFrame internally to perform the count.

C.Use 'groupBy' to group by 'customer_id' and then use the 'count' aggregate function. Do not use a UDF.

D.Create a UDF that takes an iterator of order IDs and returns the count. Apply this UDF to each partition of the DataFrame using 'maplnPartitions' .

E.Create a UDF that calculates the mode of total_amount, then apply the UDF to the DataFrame using 'select'.

Question 34

You are tasked with optimizing a Snowpark application that performs complex data transformations on a large dataset (1 TB) stored in Snowflake. The application currently uses Snowpark DataFrames and is experiencing slow performance. You suspect the issue might be related to data transfer overhead between the Snowflake engine and the Python environment. Which of the following strategies would be MOST effective in minimizing this overhead and improving performance?

A.Convert the Snowpark DataFrame to a Pandas DataFrame and perform the transformations locally within the Python environment.

B.Utilize User-Defined Functions (UDFs) written in Python to encapsulate the transformations and execute them within the Snowflake engine.

C.Increase the virtual warehouse size to the largest available option (e.g., X-Large) to improve processing power within Snowflake, regardless of data transfer costs.

D.Reduce the data volume by applying aggressive filtering and aggregation using Snowpark DataFrame operations before any other transformations, minimizing the amount of data transferred.

E.Implement vectorization techniques within the Snowpark DataFrame operations using built-in functions and optimized expressions where applicable.

Question 35

You have a Snowpark DataFrame 'products_df with columns 'product_id', 'category', and 'price'. You want to find the top 3 most expensive products within each category Which of the following Snowpark code snippets will accomplish this, using window functions?