Pass Guaranteed 2026 SPS-C01: Snowflake Certified SnowPro Specialty - Snowpark Pass-Sure Test Collection

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Snowflake SPS-C01 Exam Syllabus Topics:

SectionWeightObjectives
Data Transformations and Operations35%- User-defined logic
  • 1. Stored procedures with Snowpark
  • 2. UDFs, UDAFs, UDTFs
- Advanced operations
  • 1. Pivot and unpivot transformations
  • 2. Window functions and analytics
  • 3. Semi-structured data processing
- DataFrame manipulation
  • 1. Selection, projection, renaming, casting
  • 2. Joins, unions, set operations
  • 3. Filtering, sorting, grouping, aggregation
Snowpark API and Development30%- Python API fundamentals
  • 1. Column operations and functions
  • 2. Data persistence and writing results
  • 3. DataFrame creation from tables, views, SQL
- Multi-language support
  • 1. Java and Scala API basics
  • 2. Environment setup and dependencies
Performance and Best Practices10%- Optimization techniques
  • 1. Query pushdown and execution plans
  • 2. Caching and warehouse sizing
  • 3. Minimizing data movement
- Security and governance
  • 1. Access control and permissions
  • 2. Data protection and compliance
Snowpark Concepts and Architecture25%- Session management and connection
  • 1. Authentication and connection settings
  • 2. Create and configure Snowpark sessions
- Snowpark architecture and execution model
  • 1. Client-side vs server-side processing
  • 2. Transformations vs actions
  • 3. Lazy evaluation and DAG execution

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Efficient Snowflake - SPS-C01 - Snowflake Certified SnowPro Specialty - Snowpark Test Collection

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q361-Q366):

NEW QUESTION # 361
You have created a Snowpark Python UDF named to apply discounts based on customer purchase history. You now need to modify the UDF to accept an additional parameter for promotional codes. However, direct modification of the code on stage is restricted. How can you alter this UDF using SQL, assuming the existing UDF definition resides in the 'mydb.public' schema?

Answer: D

Explanation:
'CREATE OR REPLACE FUNCTION' is the correct SQL command to modify an existing UDF. It replaces the old definition with the new one, effectively altering the UDF's parameters and code. Renaming (option A) requires creating a new function anyway. 'ALTER FUNCTION MODIFY AS' is invalid syntax (option C). While redeploying is an option, 'CREATE OR REPLACE FUNCTION' is more efficient (option D). There is no ' ALTER FUNCTION ADD PARAMETER or 'ALTER FUNCTION SET BODY (option E) SQL syntax available for UDF modification.


NEW QUESTION # 362
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?

Answer: B

Explanation:
Option B is correct because it uses 'rangeBetween(-30, 0)' which specifies a frame that includes all rows within a range of 30 days preceding the current row, based on the ordering defined by the "date" column. Since 'date' column is used for ordering, we need to use rangeBetween' and NOT 'rowsBetween' . Also, we need to use 'sf.sum()' for Snowflake functions'. If the column is of Timestamp type, 'rangeBetween' represents days only, and in case it's numeric type represents numeric interval. Option A calculates the cumulative sum from the beginning of time for each page URL. Option C doesn't correctly specify the end of the frame. Option D has incorrect syntax and may not work. Option E uses incorrect sum function as well, as it misses sf prefix.


NEW QUESTION # 363
You are using Snowpark to process a DataFrame 'employee df containing employee data, including 'employee_id', 'name' , 'department' , and 'salary'. You need to implement a complex data cleaning and transformation pipeline that involves the following steps: 1. Remove duplicate rows based on 'employee id'. 2. Fill missing 'salary' values with the average salary for the employee's department. 3. Standardize department names by converting them to uppercase. 4. Create a new column 'salary_range' based on the salary. if Salary less than 50k 'Low', greater than 50k and less than 100k 'Medium', greater than 100k 'High'. Which of the following code snippets MOST effectively combines these transformations into a single, readable, and efficient Snowpark pipeline? Assume you have a session object available named 'session' and import necessary modules from 'snowflake.snowpark.functions as F'

Answer: D

Explanation:
Option E is the most efficient and recommended solution for the following reasons: Window Function for Filling Missing Salaries : It uses a window function ('Window.partitionBy('department')') to calculate the average salary for each department efficiently. This is more performant than joining with an aggregated DataFrame or collecting data to the client. No Client-Side Data Handling : All transformations are performed within Snowflake using Snowpark DataFrame operations. This avoids bringing data to the client, which is crucial for performance. Concise 'salary_range' Logic : It uses 'F.when' to define the 'salary_range' column in a concise and readable manner. The chained 'when' calls are a standard way to define conditional column values. Avoids UDF when not Necessary : It avoids using a UDF for calculating 'salary_range' , which generally has overhead compared to built-in functions. Option A computes the average salaries for each department and join again to the original dataframe, which requires more resources. Using UDF is also less performant when there is function available. Option B does not fill nulls before creating salary ranges. Option C collect data on Client side and is inefficient. Option D fillna method is not available and again, the UDF is less performant as it is not necessary.


NEW QUESTION # 364
You have a Snowpark DataFrame 'df' containing customer data with columns 'customer id', 'name', 'age', and 'city'. You want to filter the DataFrame to include only customers from 'New York' who are older than 30, then extract the 'customer id' and 'name' into a Rows object, and finally print the 'name' of the first row in the Rows object. Which of the following code snippets correctly achieves this using Snowpark Python?

Answer: B

Explanation:
The correct answer is C. The code first filters the DataFrame based on the specified conditions. Then, it selects the 'customer_id' and 'name' columns. The 'collect()' method retrieves the data as a list of Rows objects. Finally, correctly accesses the 'name' attribute of the first row in the list. A uses dictionary access which is incorrect for Row objects, B iterates the dataframe and does not get the first row correctly, D accesses the list by index (incorrect approach) and E is only required in scala


NEW QUESTION # 365
A data engineering team is building a Snowpark application in Python to perform advanced time series analysis on sensor data stored in Snowflake. They need to leverage a specific, but older, version of the 'pandas' library (version 1.1.5) that is not available in the default Snowflake Anaconda channel. Which of the following approaches is the MOST efficient and recommended way to ensure this specific version of 'pandas' is available to their Snowpark application, while minimizing security risks and operational overhead?

Answer: C

Explanation:
Creating a new Snowflake Anaconda environment is the most efficient and secure way to manage specific package versions. It leverages Snowflake's built-in dependency management capabilities and avoids external dependencies or manual package management within the application code. Options A and D introduce potential security risks and maintenance overhead. Option B is inefficient for dependency management. Option E will not work, as it tries to install dependencies at runtime, which isn't supported in Snowpark.


NEW QUESTION # 366
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