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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Performance Optimization and Best Practices	20%	- Debugging and explain plans - Minimizing data transfer - Vectorized UDFs - Warehouse sizing for Snowpark - Caching strategies - Query pushdown and optimization
Snowpark Concepts	15%	- Snowpark DataFrames and query plans - Snowpark Sessions and connection management - Stored procedures and conditional logic - Transformations vs. Actions - Snowpark architecture and core concepts - Client-side vs. Server-side execution
Snowpark API for Python	30%	- Establishing connections and session management - Working with Semi-structured data - DataFrame creation and manipulation - Reading and writing data - User-Defined Functions (UDFs) and Stored Procedures
Data Transformations and DataFrame Operations	35%	- Using built-in functions - Complex data pipelines - Window functions - Persisting transformed data - Filtering, Aggregating, and Joining DataFrames

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q124-Q129):

NEW QUESTION # 124
You have a Snowpark Python application that uses a UDF to perform custom data transformations. The UDF relies on a large, read-only lookup table that is stored as a CSV file on a Snowflake stage. Which of the following strategies would be the MOST efficient way to access the lookup table within the UDF?

A. Load the CSV file into a Snowflake table and then query the table from within the UDF using 'session.sql(V.
B. Read the CSV file from the stage once when the UDF is first called, cache the data in a global variable within the UDF module, and then reuse the cached data for subsequent calls.
C. Use the 'cachetoolS library with a Least Recently Used (LRU) cache to store the lookup table in memory. The UDF will check the cache before reading the CSV file, and update the cache if necessary. The CSV file is read with get_stage_file API from session.
D. Read the CSV file from the stage every time the UDF is called using 'snowflake.connector.connect()' and then load the data into a Pandas DataFrame within the UDF function.
E. Load the CSV file into a Snowflake stage, and in the python UDF code, use the get_stage_file API from session object to read the file once. Then the data cached in-memory within the UDF module, and reuse the cached data for subsequent calls.

Answer: A,C

Explanation:
Both options C and E are efficient. Option C leverages Snowflake's internal storage and query capabilities, avoiding repeated file reads. Although reading a file from stage only at once is good, it would impact the first call. Option E avoids hitting the stage every time, and only when the key is not present in cache. Options A and B suffer from performance bottlenecks due to repeated file access. In general, reading data within the Snowflake environment (C) is more performant than reading data from external sources within the UDF, especially when Snowflake's query optimizer can be used. Caching with LRU using the 'cachetoolS library is effective, as cache would contain some values from large data. get_stage_file API from session object is efficient way.

NEW QUESTION # 125
A Snowpark application needs to manage multiple sessions concurrently, each connected to a different Snowflake warehouse for resource isolation. The application receives warehouse names dynamically at runtime. How can the application efficiently create and manage these sessions while ensuring proper resource cleanup?

A. Create a global session object and use 'session.use_warehouse(warehouse_name)' before each operation. This implicitly switches the warehouse for all operations on that session.
B. Create separate sessions for each warehouse, but only close the last session created to avoid disconnecting all connections.
C. Create a single session and create multiple Snowpark DataFrames, specifying the warehouse in each DataFrame's 'createOrReplaceTempView' method.
D. Create a separate session object for each warehouse using a loop and store them in a list. Ensure each session is closed using 'session.close()' in a 'finally' block after use or using a context manager.
E. Create a single session object with connection pooling enabled and let Snowpark handle warehouse switching automatically based on the queries executed.

Answer: D

Explanation:
Option B is the most reliable and recommended way to manage multiple sessions. It ensures resource isolation by creating separate sessions per warehouse. Closing each session properly, especially using a 'finally' block or context manager, prevents resource leaks and ensures sessions are closed even if errors occur. Option A can lead to unpredictable behavior if multiple threads or asynchronous tasks interact with the global session. Options C, D and E are generally incorrect or don't properly manage resource cleanup or isolation.

NEW QUESTION # 126
Consider the following Snowpark Python code snippet intended to operationalize a stored procedure:

This code results in an error during registration. Which of the following actions would be MOST appropriate to fix the registration issue and ensure the stored procedure can be successfully operationalized?

A. Remove the 'input_data' parameter from the 'main' function definition and access the input data directly using within the function.
B. Move the 'session.sproc.register' call outside of the 'main' function and execute it separately after the 'main' function definition.
C. Add 'session.close()' at the end of the main function.
D. Define 'session' as a global variable before calling 'session.sproc.register' .
E. Add in sproc.register.

Answer: B

Explanation:
Option C is the correct solution. The 'session.sproc.registefl method should be called outside of the 'main' function definition. The registration process is a separate step from the function definition itself. The 'sproc.register' will take the function definition and create an object in Snowflake. Since this must be outside of the function it needs to be outside the main function. All other options would not solve the error with the registration.

NEW QUESTION # 127
A Snowpark application needs to process large volumes of sensor data stored in a Snowflake table named , which includes columns , 'timestamp' , and The application must calculate a rolling average of for each over a 5-minute window. The data is not perfectly ordered by 'timestamp' within each 'sensor_id'. What is the MOST efficient and accurate way to implement this rolling average calculation using Snowpark?

A. Using a Window specification with 'orderBy('timestamp')' and 'rowsBetween(Window.unboundedPreceding, Window.currentRow)' to calculate the cumulative average, then subtracting the average from 5 minutes ago. The query will then be grouped on the sensor id.
B. Using a Window specification with 'orderBy('timestamp')' and 'rowsBetween(Window.unboundedPreceding, Window.currentRow)' in conjunction with and a UDF to manually calculate the rolling average within each group.
C. Using after applying a filter to select only the data within the 5-minute window, updating the filter for each new window.
D. Using a Window specification with 0)' and the 'avg()' window function. (Where 'to_seconds' converts a duration to seconds)
E. Implementing a Python UDTF (User-Defined Table Function) that iterates through the data for each calculates the rolling average manually, and emits the results as rows.

Answer: D

Explanation:
Option D is the most efficient and accurate. 'partitionBy('sensor_id')' ensures that the rolling average is calculated separately for each sensor. 'orderBy('timestamp'Y orders the data within each partition by timestamp. 0)' defines the 5- minute window relative to the current row, accurately capturing all readings within that window even if they are slightly out of order. 'avg(Y then efficiently calculates the average within that window. Other options are either less efficient (e.g., UDTF iteration) or less accurate (e.g., incorrect window definitions, filtering).

NEW QUESTION # 128
You've written a Snowpark stored procedure in Python that utilizes a custom Python library named 'my utils.py' containing utility functions for data transformation. This library is not available in Anaconda'. You want to deploy the stored procedure and ensure that the custom library is accessible during execution. Which is the most appropriate way to operationalize your Snowpark stored procedure with the custom library?

A. Copy the contents of 'my_utils.py' directly into the stored procedure's code.
B. Install 'my_utils.py' on the Snowflake warehouse where the stored procedure will be executed.
C. Upload 'my_utils.py' to a Snowflake stage and specify the stage path in the 'imports' argument of the 'session.sproc.register' method.
D. Create a Snowpark DataFrame from 'my_utils.py' and pass it as an argument to the stored procedure.
E. Package 'my_utils.py' into a wheel file and upload it to a Snowflake stage. Then, add the stage path to the 'packages' argument of the 'session.sproc.register' method.

Answer: C

Explanation:
Option A is the most appropriate. The 'imports' argument in 'session.sproc.register' is specifically designed for including custom modules and files that are not available through Anaconda. Uploading 'my_utils.py' to a stage and referencing it in the 'imports' argument makes the library available during stored procedure execution. Packaging into a wheel is used for libraries that are available through pypi but may require a specific version. Option C is bad practice, and options D and E are impossible.

NEW QUESTION # 129
......

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