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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Performance and Best Practices	10%	- Security and governance 1. Access control and permissions 2. Data protection and compliance - Optimization techniques 1. Caching and warehouse sizing 2. Query pushdown and execution plans 3. Minimizing data movement
Topic 2: Snowpark Concepts and Architecture	25%	- Snowpark architecture and execution model 1. Lazy evaluation and DAG execution 2. Client-side vs server-side processing 3. Transformations vs actions - Session management and connection 1. Create and configure Snowpark sessions 2. Authentication and connection settings
Topic 3: Snowpark API and Development	30%	- Python API fundamentals 1. DataFrame creation from tables, views, SQL 2. Column operations and functions 3. Data persistence and writing results - Multi-language support 1. Environment setup and dependencies 2. Java and Scala API basics
Topic 4: Data Transformations and Operations	35%	- Advanced operations 1. Window functions and analytics 2. Pivot and unpivot transformations 3. Semi-structured data processing - DataFrame manipulation 1. Joins, unions, set operations 2. Filtering, sorting, grouping, aggregation 3. Selection, projection, renaming, casting - User-defined logic 1. Stored procedures with Snowpark 2. UDFs, UDAFs, UDTFs

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

Question #1

Consider the following Snowpark Python stored procedure:

What steps are necessary to register this Python code as a stored procedure named 'GET ROW COUNT in Snowflake and allow users with the 'ANALYST' role to execute it, assuming the stored procedure will be created with the 'EXECUTE AS OWNER clause, and the table name parameter will be passed dynamically during invocation?

A. 1. Create the stored procedure using 'CREATE OR REPLACE PROCEDURE GET ROW COUNT(VARCHAR) RETURNS VARCHAR LANGUAGE PYTHON EXECUTE AS OWNER AS $$ import snowflake.snowpark as snowpark def snowpark.Session, table_name: str) str: df = session.table(table_name) count = df.count() return f" Table {table_name} has {count} rows." $$; 2. Grant 'USAGE privilege on the database and schema containing the stored procedure to the 'ANALYST role. 3. Grant 'EXECUTE PROCEDURE privilege on the 'GET ROW COUNT stored procedure to the 'ANALYST role.
B. 1. Create the stored procedure using 'CREATE OR REPLACE PROCEDURE GET ROW COUNT(VARCHAR) RETURNS VARCHAR LANGUAGE PYTHON IMPORTS-('/path/to/dependencies.zip') EXECUTE AS OWNER;' 2. Grant 'USAGE' privilege on the database and schema containing the stored procedure to the 'ANALYST' role. 3. Grant 'EXECUTE PROCEDURE privilege on the 'GET ROW COUNT' stored procedure to the 'ANALYST role.
C. 1. create the stored procedure using 'CREATE OR REPLACE PROCEDURE RETURNS VARCHAR LANGUAGE PYTHON IMPORTS-('/path/to/dependencies.zip') EXECUTE AS OWNER;' 2. Grant 'USAGE-' privilege on the database and schema containing the stored procedure to the 'ANALYST role. 3. Grant ' EXECUTE privilege on the stored procedure to the 'ANALYST role.
D. 1. create the stored procedure using 'CREATE OR REPLACE PROCEDURE RETURNS VARCHAR LANGUAGE PYTHON RUNTIME_VERSlON=3.8 HANDLER='my_sproc' EXECUTE AS OWNER$ 2. Grant 'USAGE privilege on the database and schema containing the stored procedure to the 'ANALYST role. 3. The 'ANALYST' role does not need any additional privilege if EXECUTE AS OWNER is set, as it's using the owner's access rights.
E. 1. Create the stored procedure using 'CREATE OR REPLACE PROCEDURE GET ROW COUNT(VARCHAR) RETURNS VARCHAR LANGUAGE PYTHON EXECUTE AS CALLER;' 2. Grant 'USAGE privilege on the database and schema containing the stored procedure to the 'ANALYST role. 3. Grant 'SELECT' privilege on all possible tables referenced by 'table_name' to the 'ANALYST' role.

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Correct Answer: B 🗳️

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Question #2

You are building a Snowpark application using the 'snowflake-cli' to manage Snowflake connections. You have configured multiple connection profiles using 'snowflake connection add'. Which of the following Python code snippets demonstrates the most efficient and idiomatic way to create a Snowpark session using a specific connection profile named 'my_profile' defined in your 'snowflake-cli' configuration? Assume snowflake-cli is correctly configured and authenticated.

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Correct Answer: D 🗳️

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Question #3

You are using Snowpark Python to create a DataFrame from an existing Snowflake table "SALES DATA'. You want to apply a user- defined function (UDF) to each row of the DataFrame to calculate a custom sales metric. The UDF requires access to the 'session' object. Which of the following approaches is correct for defining and applying the UDF in Snowpark?

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Correct Answer: B 🗳️

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Question #4

When creating UDFs/UDTFs in Snowpark Python, what are the advantages of explicitly specifying data types (either via Python type hints or the registration API) compared to relying on implicit type inference?

A. Automatic data type conversion by Snowflake, eliminating the need for explicit casting within the UDF/UDTF.
B. Reduced deployment time.
C. Improved performance due to reduced overhead in data type resolution at runtime.
D. Enhanced code readability and maintainability, making it easier to understand the expected data types.
E. Early detection of type-related errors during development, preventing runtime failures.

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Correct Answer: C,D,E 🗳️

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Question #5

You have a Python function that takes a string as input and returns a sentiment score (a float between -1 and 1). This function relies on a large pre-trained Natural Language Processing (NLP) model. You want to deploy this function as a UDF in Snowpark and optimize its performance, specifically minimizing the model loading time for each execution. You have already uploaded the model to a stage named '@my_stage/models'. Select the option that combines caching techniques and UDF deployment strategies to achieve the best performance.