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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider a Snowpark DataFrame with columns 'DEPARTMENT, 'SALARY , and 'YEAR. You want to find the average salary for each department over all years and then filter the departments to only include those where the average salary is greater than 100000. Which of the following approaches is the MOST efficient and correct way to achieve this using Snowpark Python?

A)

B)

C)

D)

E)

2. You have a Snowpark DataFrame 'sales df with columns 'product_id' (INTEGER), 'sale date' (DATE), and 'sale_amount' (DOUBLE). You need to filter the DataFrame to include only sales that occurred in the year 2023 and where the 'sale_amount' is greater than the average 'sale_amount' for that specific 'product id'. Which of the following Snowpark Python code snippets will correctly achieve this?

A)

B)

C)

D)

E)

3. Consider a Snowflake table 'orders' with columns 'order_id', 'customer_id', 'order_date', and 'status'. You need to update the 'status' of all orders placed before January 1, 2023, to 'Archived'. Which of the following approaches is the most efficient and idiomatic way to achieve this using Snowpark DataFrames, assuming 'orders df DataFrame represents the 'orders' table?

A) Option E
B) Option C
C) Option B
D) Option A
E) Option D

4. You have a Python function that calculates a complex statistical measure on a given row of a DataFrame. You want to apply this function to each row of a Snowpark DataFrame in a distributed manner. Which of the following is the MOST efficient way to achieve this?

A) Use the "map' method on the Snowpark DataFrame's underlying RDD (Resilient Distributed Dataset) and pass the Python function as an argument.
B) Use Snowpark's 'sprocs feature to create stored procedure and call the Python function.
C) Create a Pandas UDF (User-Defined Function) using decorator and apply it to the Snowpark DataFrame.
D) Iterate through the rows of the Snowpark DataFrame and call the Python function on each row individually.
E) Use the 'apply' method on the Snowpark DataFrame, passing the Python function as an argument.

5. You're working with Snowpark and want to load data from a Pandas DataFrame into a Snowpark DataFrame. The Pandas DataFrame, 'customer_data' , contains columns with mixed data types (integers, strings, dates). Some columns also contain NULL values. You need to ensure that the data types are correctly inferred by Snowpark, NULL values are handled appropriately, and the resulting Snowpark DataFrame 'snowpark_customers' can be used for further transformations. What is the best approach to achieve this with minimal code and maximum performance?

A) Infer the schema explicitly before creating the Snowpark DataFrame using Pandas DataFrame column types. For the string columns, define them to be StringType().
B) First, replace all NA/NaN values in Pandas DataFrame with None, then create Snowpark DataFrame using 'session.createDataFrame(customer_datay.
C) Use 'session.write_pandas' because its optimized for large pandas dataframe.
D) Explicitly define the schema with StructType and StructField, specifying the column names and data types based on the Pandas DataFrame, converting null values to Snowflake's null representation during DataFrame creation.
E) Use 'session.createDataFrame(customer_datay and rely on Snowpark to automatically infer the schema and handle NULL values implicitly. Convert any problematic columns after the Snowpark DataFrame is created.

Solutions: