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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Which of the following are key benefits of using Snowpark's server-side execution capabilities for data processing tasks within Snowflake, compared to performing the same tasks on the client-side? (Select all that apply)

A) Increased processing speed due to leveraging Snowflake's scalable compute resources.
B) Greater flexibility in choosing programming languages for data processing.
C) Improved security by keeping data within Snowflake's secure environment.
D) Reduced data transfer costs between the client and Snowflake.
E) Simplified code deployment and maintenance.

2. You have a Snowpark application that processes sensor data from a large number of devices. You've implemented a scalar UDF in Python to calculate a complex statistical metric for each sensor reading. Initial tests show poor performance. Which of the following strategies would be MOST effective in improving the performance of this application, considering the nature of the computation and the data volume?

A) Rewrite the UDF logic directly in SQL, leveraging Snowflake's built-in functions for statistical calculations.
B) Increase the warehouse size significantly to provide more resources for the scalar UDF execution.
C) Replace the Python scalar UDF with a Java scalar UDF, as Java typically offers better performance than Python in Snowflake.
D) Implement caching for the UDF results, assuming the statistical metric calculations are frequently repeated for the same sensor readings.
E) Replace the Python scalar UDF with a Python vectorized UDF, ensuring the UDF processes data in batches instead of row-by-row.

3. You are tasked with building a Snowpark application that processes sensor data. The data arrives continuously and is ingested into a Snowflake table called 'RAW SENSOR DATA'. You need to create a Snowpark DataFrame that applies a user-defined function (UDF) to each row to enrich the data. The UDF, named 'ENRICH SENSOR DATA, is written in Python and resides in a stage called 'UDF STAGE. The UDF takes three arguments: 'timestamp', and 'raw_value', all of which are STRING type in Snowflake. Which of the following code snippets correctly defines and calls the UDF using Snowpark?

A)

B)

C)

D)

E)

4. You are developing a Snowpark Python application that reads data from a Snowflake table, performs several transformations including filtering, aggregation, and joining with another DataFrame, and then writes the results back to a new table. You want to optimize the execution plan to minimize data movement and processing time. Which of the following strategies would be MOST effective in leveraging Snowpark's lazy evaluation capabilities to achieve this optimization?

A) Calling 'cache()' on the initial DataFrame read from the table to materialize it in memory before any transformations.
B) Calling after each transformation to materialize intermediate results and then creating new DataFrames for subsequent operations.
C) Executing each transformation in separate Python processes using multiprocessing to parallelize the workload.
D) Chaining all the transformations together using DataFrame methods (e.g., 'filter()' , 'groupBy()' , 'join()') and only calling or at the very end.
E) Defining all transformations in a single, complex SQL query string and using to execute it.

5. You have a Snowflake table 'orders_json' with a VARIANT column named "order details'. This column contains JSON objects, and one of the fields within these objects is an array called 'items'. You need to use Snowpark to flatten the 'items' array into rows, extracting the 'item_id' , 'item_name' , and 'quantity' from each item in the array. Which of the following Snowpark code snippets will correctly achieve this, assuming 'df is a DataFrame representing 'orders_json'?

A)

B)

C)

D)

E)