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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Snowpark API and Development	30%	- Multi-language support 1. Java and Scala API basics 2. Environment setup and dependencies - Python API fundamentals 1. Data persistence and writing results 2. Column operations and functions 3. DataFrame creation from tables, views, SQL
Performance and Best Practices	10%	- Optimization techniques 1. Query pushdown and execution plans 2. Caching and warehouse sizing 3. Minimizing data movement - Security and governance 1. Data protection and compliance 2. Access control and permissions
Data Transformations and Operations	35%	- Advanced operations 1. Pivot and unpivot transformations 2. Semi-structured data processing 3. Window functions and analytics - User-defined logic 1. UDFs, UDAFs, UDTFs 2. Stored procedures with Snowpark - DataFrame manipulation 1. Joins, unions, set operations 2. Filtering, sorting, grouping, aggregation 3. Selection, projection, renaming, casting
Snowpark Concepts and Architecture	25%	- Snowpark architecture and execution model 1. Transformations vs actions 2. Client-side vs server-side processing 3. Lazy evaluation and DAG execution - Session management and connection 1. Create and configure Snowpark sessions 2. Authentication and connection settings

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

Question #1

You have a Snowpark Python application that reads data from multiple Snowflake tables, performs complex transformations using UDFs, and writes the results to a new table. During peak hours, the application experiences performance bottlenecks. The Snowflake warehouse associated with the Snowpark session is already configured with the 'SNOWPARK OPTIMIZED warehouse type. Which of the following strategies, when implemented together, would BEST improve the application's performance?

A. Increase the size of the Snowpark-optimized warehouse and enable auto-scaling with a minimum of 1 node and a maximum of 2 nodes.
B. Increase the size of the Snowpark-optimized warehouse, utilize vectorized UDFs where applicable, and consider using Snowpark's optimized join operations (if available).
C. Enable query acceleration and increase the 'MAX CONCURRENCY LEVEL' session parameter.
D. Use the 'CACHE RESULT clause for frequently accessed data and rewrite UDFs in SQL instead of Python.
E. Increase the size of the Snowpark-optimized warehouse, partition the input tables based on relevant join keys, and optimize UDFs for CPU efficiency.

Question #2

You're tasked with creating a Snowpark UDF to calculate the Haversine distance between two sets of latitude and longitude coordinates (point A and point B). Which of the following statements about deploying and using this UDF is/are TRUE?

A. The UDF can only be written in Python and must be deployed as an inline UDF within the Snowpark session.
B. The UDF can only be called directly from within the Snowpark session and cannot be used in standard Snowflake SQL queries.
C. The UDF can be written in Python, Java, or Scala. Using a Java UDF will likely offer best performance, especially when dealing with very large datasets. You'll need to stage the compiled JAR file on an internal stage that Snowpark can access.
D. The UDF, once defined, can be used inside of any DataFrame operation like 'select', 'filter' , and 'withColumn'
E. When defining the UDF with input types, the Python types must exactly match the corresponding Snowflake data types.

Question #3

A data engineering team wants to create a Snowpark stored procedure that takes a VARIANT column from a Snowflake table, parses a specific JSON element within each row, and returns a new DataFrame with the extracted data as a STRING column. The JSON structure is consistent across all rows. What is the MOST efficient and type-safe way to implement this, considering the need for performance and maintainability?

A. Use Snowpark's 'get' function within the stored procedure to extract the JSON element, explicitly cast the extracted value to STRING using 'cast('string')' , and register the stored procedure with defining the output schema.
B. Define the input column using and use the operator to implicitly convert the extracted JSON element to a string, relying on Snowpark's type inference for the return type.
C. Use Python type hints for the input VARIANT column, extract the JSON element using string manipulation within the stored procedure, and return a DataFrame with the extracted data as a string.
D. Use the 'get' function on the VARIANT column to extract the JSON element, use the 'as_varchar' function to cast the VARIANT value to a String value, and register the stored procedure with explicit 'return_type' and schema definition for enhanced type safety
E. Define the input column as a generic 'object' type in Python, use Snowpark's 'get function with path navigation to extract the JSON element, and return the extracted data as a string using 'as_varchar'.

Question #4

You are developing a Snowpark Python application that reads a large dataset (1 TB) from a Snowflake table 'TRANSACTIONS and performs complex aggregations. The application is experiencing significant performance issues, with query execution taking several hours. You have already verified that the warehouse size is appropriate and caching is enabled. You suspect the issue might be related to data skew and incorrect partitioning. Which of the following strategies would be MOST effective in identifying and mitigating this performance bottleneck?

A. Implement caching using after reading the data from the 'TRANSACTIONS' table and before performing any aggregations.
B. Analyze the 'TRANSACTIONS' table's data distribution using and histograms on the join keys. Based on the analysis, use with the most skewed column to redistribute the data more evenly. Also, consider using bucketing if appropriate.
C. Use to force a broadcast join, assuming the aggregated data is small enough to fit in memory. Monitor query profiles to confirm the broadcast occurs.
D. Use partition_expression=sf.rand())' to randomly repartition the DataFrame into 100 partitions, regardless of the data distribution in the ' TRANSACTIONS table.
E. Increase the Snowflake warehouse size to the largest available option (e.g., X6-Large) to provide more resources for query execution, without analyzing data distribution.

Question #5

Consider a Snowpark DataFrame with columns 'DEPARTMENT, 'SALARY , and 'YEAR. You want to find the average salary for each department over all years and then filter the departments to only include those where the average salary is greater than 100000. Which of the following approaches is the MOST efficient and correct way to achieve this using Snowpark Python?

A.

B.

C.

D.

E.