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Snowflake SPS-C01 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Testing, Debugging, and Deployment | - Production readiness
|
| Data Engineering with Snowpark | - Pipeline development
|
| Performance Optimization and Best Practices | - Efficient Snowpark execution
|
| DataFrame Operations and Data Processing | - Data transformation workflows
|
| Snowpark Fundamentals | - Snowpark architecture and concepts
|
| User Defined Functions and Stored Procedures | - Extending Snowpark with custom logic
|
Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:
1. You have a Snowpark DataFrame with columns 'sale_date', 'product_id', and 'revenue'. You need to calculate the cumulative revenue for each product over time. Which of the following approaches will accomplish this in Snowpark using window functions?
A)
B)
C)
D)
E) 
2. You have a Snowpark application that utilizes a vectorized Python UDF to perform complex calculations on a large dataset. You notice that the performance is still not optimal. You suspect that the bottleneck might be related to how the data is being partitioned and processed by Snowflake. Which of the following actions, when performed in conjunction with vectorization, would MOST likely improve performance?
A) Ensure that the data is pre-sorted according to the primary key of the table before applying the UDF.
B) Convert the DataFrame to a Pandas DataFrame before applying the UDF.
C) Repartition the Snowpark DataFrame using to align the data distribution with the computational needs of the UDF.
D) Increase the number of UDF worker threads within the UDF definition.
E) Broadcast the DataFrame to all compute nodes before applying the UDF.
3. You have a Snowpark DataFrame 'df_orders' containing order data'. You want to delete all records from the underlying Snowflake table 'ORDERS TABLE' where the 'order_date' is older than '2023-01-01' using a Snowpark DataFrame operation. Which of the following code snippets is the MOST efficient and recommended way to achieve this, assuming 'spark' is your Snowpark Session object?
A) Option E
B) Option D
C) Option A
D) Option B
E) Option C
4. You have a Snowpark DataFrame 'employees df representing employee data'. You need to update the 'salary' column for employees in the 'Sales' department by applying a 10% increase. Which of the following Snowpark code snippets correctly performs this update? Assume a Snowflake table named 'employees' exists and 'employees df' is correctly created from it.
A) Option E
B) Option D
C) Option A
D) Option B
E) Option C
5. You are developing a Snowpark application that needs to access data from a Snowflake table called 'EMPLOYEES. You want to create a Snowpark DataFrame representing this table. However, you are facing issues with the connection and believe that the database, schema, or warehouse attributes may not be set up correctly for the session. Which of the following code snippets, used in conjunction, BEST demonstrates how to create a Snowpark session with robust error handling to identify and address potential connection issues before attempting to create the DataFrame?
A)
B)
C)
D)
E) 
Solutions:
| Question # 1 Answer: A,D | Question # 2 Answer: C | Question # 3 Answer: D | Question # 4 Answer: A,E | Question # 5 Answer: D,E |
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