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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

Question 1

You are developing a Snowpark application that needs to access data from a Snowflake table called 'EMPLOYEES. You want to create a Snowpark DataFrame representing this table. However, you are facing issues with the connection and believe that the database, schema, or warehouse attributes may not be set up correctly for the session. Which of the following code snippets, used in conjunction, BEST demonstrates how to create a Snowpark session with robust error handling to identify and address potential connection issues before attempting to create the DataFrame?

A.

B.

C.

D.

E.

Question 2

You are tasked with building a Snowpark application that receives a DataFrame 'new customers_df containing customer data'. Your application needs to insert this data into the 'CUSTOMERS' table in Snowflake. The 'CUSTOMERS table has columns 'CUSTOMER ONT), 'NAME' (VARCHAR), and 'JOIN DATE' (DATE). However, contains all columns as VARCHAR. Which of the following approaches ensures the correct data types are inserted into the 'CUSTOMERS' table, minimizing errors and maximizing performance? Assume the 'session' object is already defined and a valid connection exists.

A. Option E
B. Option A
C. Option B
D. Option D
E. Option C

Question 3

You have a Snowpark DataFrame named 'employee_df in VS Code. You want to define a user-defined function (UDF) in Python that calculates the bonus for each employee based on their salary and performance rating. The UDF should take the salary (SALARY) and performance rating (PERFORMANCE RATING) as inputs and return the bonus amount. Assume you have already established a Snowpark session. Which code snippet accurately defines and registers a UDF named 'calculate_bonus' that can be applied to the 'employee_df DataFrame?

A.

B.

C.

D.

E.

Question 4

Consider the following Snowpark Python code snippet designed to calculate a custom metric on financial data, using a vectorized UDF for performance. Identify potential performance bottlenecks and recommend optimization strategies.

Which of the following actions (may be more than one) would MOST likely improve the performance of this Snowpark application?

A. Ensure that the 'financial_data' table is clustered by 'ticker_symbor to optimize the 'groupBy' operation.
B. Change 'FloatType' to 'DoubleType' as it provides more precision.
C. Repartition the 'data' DataFrame by 'ticker_symbol' before the 'groupBy' operation using to improve data locality.
D. Use to explicitly declare the Pandas dependency.
E. Use before writing to the 'metric_table' , especially if the table is subsequently used downstream.

Question 5

You have created a Python UDTF in Snowpark to process large volumes of image data'. This UDTF resizes each image and extracts certain features from it. The process is memory-intensive and sometimes fails due to Python process exceeding memory limits. You need to optimize this UDTF for memory usage. Which of the following strategies would be MOST effective? (Select TWO)

A. Leverage external packages such as PILIPillow with appropriate image compression techniques to reduce the memory footprint of each image before processing.
B. Increase the value of 'MAX BATCH SIZE in the Snowpark session configuration to allow the UDTF to process larger batches of images at once.
C. Utilize a smaller Snowflake warehouse size. Smaller warehouses have less memory per node, which will force the UDTF to process data in smaller chunks.
D. Use a scalar UDF instead of a UDTF. Scalar UDFs are generally more memory efficient.
E. Implement lazy evaluation and iterators within the UDTF to process images one at a time instead of loading all images into memory at once.

Solutions: