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Snowflake SPS-C01 Exam Syllabus Topics:

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

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q196-Q201):

NEW QUESTION # 196
Consider the following Python code snippet using Snowpark:

Which of the following statements are true regarding this Snowpark code?

A. The code uses best practices by explicitly closing the Snowflake session, preventing resource leaks.
B. The code will fail because password authentication is deprecated and replaced by Key Pair authentication.
C. The code calculates the sum of the 'sales' column, grouped by 'category' , and saves the result to a new table named , overwriting it if it exists.
D. The code establishes a connection to Snowflake using the provided credentials.
E. The code reads data from a table named 'my_table' in Snowflake.

Answer: A,C,D,E

Explanation:
The code correctly connects to Snowflake, reads data from 'my_table', performs aggregation, and saves the result to 'agg_sales_table' with overwrite mode, and closes the session. While password authentication is discouraged, it is still supported. The other options accurately describe the code's functionality. Closing the session is a best practice to release resources.

NEW QUESTION # 197
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 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.
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 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.
D. 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.
E. 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.

Answer: B

Explanation:
Option A correctly outlines the process. First, the stored procedure is created with the correct syntax, including specifying the runtime version and handler, and using 'EXECUTE AS OWNER. Second, 'USAGE privilege is granted on the database and schema. Third, EXECUTE PROCEDURE' privilege must be explicitly granted to the 'ANALYST role. Option B attempts to embed the python code inline but doesn't include IMPORTS section if needed and its more common now to upload the Python code using create or replace procedure syntax. Option C is incorrect because you need to grant ' EXECUTE PROCEDURE privilege to the role in addition to Usage. Option D uses 'EXECUTE AS CALLER , which is not what the question specifies and requires granting 'SELECT privileges on the underlying tables, defeating the purpose of owner rights. 'EXECUTE' is not a valid privilege to grant on stored procedures in Snowflake (Option E), it has to be 'EXECUTE PROCEDURE.

NEW QUESTION # 198
You have two Snowpark DataFrames, 'dfl' and 'df2 , both containing customer data, but with slightly different schemas. 'dfl' has columns 'customer_id', 'name', and 'email'. 'df2' has columns 'id', 'customer name', and 'email_address'. You want to perform a set- based operation to find all unique customer IDs present in 'dfl but NOT in 'df2' , considering that 'customer_id' in 'dfl corresponds to 'id' in 'df2. Which of the following code snippets will achieve this, ensuring that column names are correctly aligned before the operation?

Answer: C

Explanation:
Option D is the correct solution. First, 'customer_id')' renames the 'id' column in 'df2 to 'customer_id', aligning it with the 'customer_id' column in 'dfl Then, 'cifl performs the set difference operation, returning only the 'customer_id' values present in 'dfl& but not in the modified 'df2. 'exceptAll' (Option A) will include duplicates. Option B uses 'minus' which does not exist on Snowpark DataFrame. Options C uses 'subtract which also does not exist. Option E will cause unexpected results because the column name of dfl and df2 would be different.

NEW QUESTION # 199
A Snowpark application processes streaming data from Kafka, performing complex windowing aggregations. The application is configured with auto-scaling enabled for the virtual warehouse. During peak hours, the application exhibits high latency despite the warehouse scaling up. Upon investigation, you observe sustained high CPU utilization on the single active warehouse. Which actions, alone or in combination, would MOST effectively improve performance while minimizing cost?

A. Increase the MAX CLUSTER COUNT parameter for the virtual warehouse. This ensures that the warehouse can scale out to a greater number of clusters to handle the increased workload.
B. Increase the MIN_CLUSTER_COUNT parameter to pre-warm additional clusters. This ensures that clusters are readily available when the workload increases, reducing latency.
C. Decrease the SCALING_POLICY parameter to reduce the time it takes for warehouses to autoscale. This will allow warehouses to keep up with processing as volume increases.
D. Repartition the input data to distribute the workload more evenly across the available clusters. Ensure the partitioning key is suitable for the aggregations being performed.
E. Optimize the Snowpark code by using vectorization and efficient data structures. This reduces the CPU load for each processing task.

Answer: D,E

Explanation:
Optimizing the Snowpark code (Option B) reduces the overall CPU load, while repartitioning the data (Option C) distributes the work more evenly across multiple clusters, reducing hotspots and enabling parallel processing. Increasing MAX_CLUSTER_COUNT (Option A) only helps if the workload is already evenly distributed. Increasing MIN_CLUSTER COUNT (Option D) may add to cost without addressing the underlying code issues or data skew. Decreasing SCALING_POLICY (Option E) may help with responsiveness but isn't a fundamental solution.

NEW QUESTION # 200
You are tasked with creating a Snowpark DataFrame from a series of large Parquet files stored in an external stage 'my_stage' . The files contain customer transaction data, but some files are corrupted and cause errors during DataFrame creation. You want to implement a solution that skips the corrupted files and logs the filenames of those files to a table named 'failed_files'. Assuming you have a Snowpark session 'session' and a UDF that inserts filenames into the 'failed_files' table, which of the following approaches is the MOST efficient and robust way to achieve this, while minimizing impact on performance and maintaining data integrity? Consider that you don't have direct control over the file format and data quality within the stage.

A. Use 'session.read.parquet('stage://my_stage/ within a try-except block to catch errors. Inside the 'except' block, call with the filename. Retry the read operation for remaining files after removing the failing file from stage.
B. Implement a custom file listing function using 'session.sql('LlST to identify potentially corrupted files by checking file size or metadata, then exclude these files when creating the Snowpark DataFrame. Use 'session.read.parquet' with the filtered list of files.
C. Use the command in Snowflake to load the Parquet files into a temporary table, specifying the 'ON ERROR = CONTINUE option. Then, create a Snowpark DataFrame from the temporary table. Log any rejected files using a 'VALIDATION MODE = RETURN ERRORS' copy command before creating the temporary table.
D. Create a Snowpark DataFrame using 'session.read.option('mode', 'PERMISSIVE').parquet('stage://my_stage/ ')'. This automatically skips corrupted records within valid files but doesn't handle entire corrupted files. Afterward, compare the counts of each file before and after processing to identify corrupted files based on lost records.

Answer: C

Explanation:
Option C is the most efficient and robust. 'COPY INTO with = CONTINUE directly leverages Snowflake's optimized loading capabilities to handle file-level errors gracefully. The 'VALIDATION_MODE allows identifying errored files before the load process. A, B, D and E involve more complex and potentially less efficient workarounds within Snowpark itself.

NEW QUESTION # 201
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