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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are tasked with optimizing a Snowpark application that performs complex geospatial calculations on a large dataset of location coordinates. The application is currently running on a standard Snowflake warehouse. Initial tests indicate that the application is CPU- bound. Which of the following actions would be MOST effective in improving the performance of this Snowpark application?

A) Switching to a Snowpark-optimized warehouse and increasing the warehouse size.
B) Enable result caching and reduce the amount of data being processed
C) Switching to a larger Snowflake warehouse size (e.g., from X-Small to Small) with the same warehouse type.
D) Partitioning the location data based on latitude and longitude and leveraging Snowpark's DataFrame API for filtering data before geospatial calculations.
E) Increasing the value of parameter at the account level.

2. You have a DataFrame 'df in Snowpark containing order data, including a VARIANT column named 'order details'. The 'order detailS column contains a nested JSON structure with fields like 'customer id' (always a string), 'items' (an array of item IDs, sometimes numbers, sometimes strings), and 'total amount' (inconsistent data type - sometimes string, sometimes number). You need to perform the following transformations: 1. Extract the "customer_id' and cast it to an integer. 2. Extract the first item ID from the 'items' array, attempting to cast it to an integer, handling potential casting errors. 3. Extract the 'total_amount' and cast it to a decimal (precision 10, scale 2), handling potential casting errors. Which of the following code snippets correctly implements these transformations using Snowpark?

A)

B)

C)

D)

E)

3. You are migrating a Pandas-based data processing pipeline to Snowpark to leverage Snowflake's scalability and performance. One part of the pipeline involves a computationally intensive custom function that is applied row-by-row to a DataFrame using the 'apply' method in Pandas. When migrating this to Snowpark, what are the most effective strategies for achieving similar functionality while maximizing performance within the Snowflake environment?

A) Rewrite the custom function as a vectorized operation using Snowpark DataFrame functions and expressions, avoiding row-by-row processing.
B) Utilize Snowpark's Pandas API to seamlessly execute the Pandas code within the Snowflake environment with minimal modifications.
C) Directly translate the Pandas 'apply' operation to a Snowpark 'apply' operation, assuming that Snowpark's implementation is automatically optimized for distributed execution.
D) Create a Snowpark User-Defined Function (UDF) using Python and apply it to the DataFrame using the 'select method, leveraging Snowflake's distributed execution capabilities.
E) Use a stored procedure to execute the pandas 'apply' row by row on the data from snowflake table.

4. A Snowpark application frequently experiences performance degradation during peak hours. You suspect that resource contention on the virtual warehouse is the primary cause. Which of the following actions, when implemented together, would BEST address this issue and ensure consistent performance?

A) Implement workload management rules to prioritize Snowpark workloads during peak hours.
B) Increase the AUTO_SUSPEND parameter of the virtual warehouse to a shorter duration to release resources more quickly.
C) Enable query acceleration for all Snowpark queries.
D) Create separate virtual warehouses for different Snowpark workloads and apply resource monitors to limit credit consumption.
E) Convert all Snowpark DataFrames to views to reduce computational overhead.

5. You are working with a Snowpark DataFrame containing website traffic data'. The DataFrame has columns like 'date' , 'page_url', and 'visit_count'. You need to calculate the cumulative sum of visit counts for each 'page_url' over time (i.e., ordered by 'date'). However, you only want to consider data from the last 30 days for each calculation. Which of the following Snowpark code snippets will correctly achieve this using window functions with a frame specification?

A)

B)

C)

D)

E)

Solutions: