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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider the following Snowpark code snippet that aims to calculate the rank of each employee based on their salary within their respective department. What are potential issues with this code, and how can you improve it? (Select all that apply.)

A) It is missing the 'col' function call in the orderBy clause. It should be 'orderBy(sf.col("salary").desc())'.
B) The code does not handle potential null values in the salary column. Consider using or before calculating the rank.
C) The 'rank()' function will produce dense ranks, which might be undesirable if there are ties in salary. Use for contiguous ranks instead.
D) The code is correct and will produce the desired output without any issues.
E) There may be performance issues if the employee table is very large. Consider adding a filter to the DataFrame before applying the window function.

2. Consider the following Snowpark code snippet:

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

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

3. You are developing a Snowpark application that processes large volumes of JSON data from an external stage. Initial testing on a MEDIUM warehouse results in significant query queuing. You suspect the issue is CPU bound due to complex JSON parsing and UDF execution within Snowpark. Considering only warehouse sizing options and assuming cost is a secondary concern to performance during peak processing hours, which strategy is MOST effective for optimizing performance? Consider the impact on concurrency.

A) Scale out to multiple MEDIUM warehouses using auto-scaling. This increases concurrency, allowing more queries to run simultaneously, but might not address CPU-bound operations within a single query.
B) Upgrade the warehouse to a LARGE. This provides more CPU and memory for the existing workload, potentially resolving the bottleneck and improving overall throughput.
C) Upgrade to an X-LARGE or higher warehouse, leveraging the increased resources to handle complex parsing and UDF execution more efficiently. Monitor CPU utilization after the upgrade.
D) Scale down to a SMALL warehouse. Smaller warehouses are optimized for smaller operations and can process certain types of operations faster. This could improve latency.
E) Implement query acceleration using materialized views to pre-compute JSON parsing results. Then, add warehouses as needed for concurrent requests

4. You are using Snowpark Python to analyze sales data stored in a Snowflake table named 'SALES DATA. The table has columns PRODUCT ICY, 'REGION', and 'SALE DATE. You need to calculate the total sale amount for each product in each region. You intend to use the 'group_by' and 'agg' functions. Which of the following Snowpark Python code snippets correctly performs this aggregation and renames the aggregated column to 'TOTAL SALES'? (Assume 'session' is a valid Snowpark session object.)

A)

B)

C)

D)

E)

5. You are tasked with optimizing the performance of a Snowpark Python application that performs complex data transformations on a large dataset of IoT sensor readings. The application uses a Snowpark-optimized warehouse. You notice that the application is consistently slow, with CPU utilization on the warehouse fluctuating significantly. Which of the following actions would be MOST effective in addressing this performance issue? Assume the dataset is partitioned on the 'sensor_id' column within Snowflake.

A) Repartition the Snowpark DataFrame using partition_expression='sensor_id')' before applying transformations. Then, explicitly colocate similar operations.
B) Ensure the Snowpark DataFrame transformations are pushed down to Snowflake as much as possible by avoiding actions like 'collect()' until absolutely necessary and leverage stored procedures.
C) Increase the warehouse size to a larger instance (e.g., from X-Small to Small). This will provide more CPU and memory resources.
D) Enable auto-scaling on the warehouse with a minimum of 2 and maximum of 5 clusters. This will allow the warehouse to dynamically adjust capacity based on workload.
E) Rewrite the Snowpark DataFrame transformations using only built-in Snowpark functions and avoid using User-Defined Functions (UDFs) written in Python.

Solutions: