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Snowflake SPS-C01 Exam Syllabus Topics:

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

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Which of the following statements are correct regarding account identifiers and their usage when creating Snowpark sessions in Python?

A) Account identifiers can only be used when connecting to Snowflake accounts in the same AWS region.
B) If the account identifier includes the region ID, you do not need to specify the region separately in the connection parameters.
C) Account identifiers are case-sensitive and must be entered exactly as provided by Snowflake.
D) The account identifier can be specified using either the 'Organization Name-Account Name' format or the legacy account locator, depending on the Snowflake account configuration and the region.
E) Using the 'Organization Name-Account Name' format for the account identifier is only valid for accounts that have been recently created.

2. Consider the following Snowpark code snippet designed to create a temporary table:

A developer encounters an error when calling this function. The error message indicates that the table already exists. How should the developer modify the code to handle this scenario gracefully, preventing the error and ensuring the temporary table is either created or overwritten?

A) Add the 'mode='ignore" option to the function. This will silently skip the creation if the table already exists.
B) First drop the table using 'session.sql(fDROP TABLE IF EXISTS {table_name}')' before calling .
C) Add the 'mode='overwrite" option to the function. This will replace the existing table with the new data.
D) Add the 'mode='append" option to the function. This will append the data to the existing table.
E) Use to create the temporary table.

3. You have a Snowpark DataFrame named 'products_df' with columns 'product_id' (INT), 'product_name' (VARCHAR), and 'price' (FLOAT). You want to create a new DataFrame called 'discounted_products df that includes all columns from 'products_df' plus a new column named 'discounted_price', which is calculated as the original price minus a discount percentage specified by the variable 'discount_rate' (e.g., 0.1 for 10%). The 'discount_rate' is stored in the database table named 'discount_table'. You want to load the rate to variable. Choose the correct ways to achieve this. (Select all that apply)

A)

B)

C)

D)

E)

4. You have a complex Snowpark Python UDF that aggregates data from various sources and returns a dictionary containing several metrics (e.g., '{'average price': 12.50, 'total sales': 1000, 'customer count': 50}'). You need to operationalize this UDF and ensure proper data type handling for each metric. Which of the following is the MOST appropriate way to define the return type using the registration API?

A) Define the return type as 'StringType' and serialize the dictionary to JSON within the UDF.
B) Use a 'MapType' with 'StringType' as the key type and 'VariantType' as the value type.
C) Use a single 'VariantType' to represent the entire dictionary.
D) Use a single 'ArrayType' to represent the entire dictionary. 'Integer Type').
E) Define a 'StructType' with ' StructFielcf for each metric, specifying the appropriate data type (e.g.,

5. You are working with Snowpark and a DataFrame named 'orders df that contains order data, including a column named 'items' which is a VARIANT type and holds an array of JSON objects, where each object represents an item in the order. You need to explode this array into separate rows, extracting the 'item_id' and 'quantity' for each item. Which of the following Snowpark snippets correctly performs this transformation AND handles potential NULL or empty arrays in the 'items' column?

A)

B)

C)

D)

E)