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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

Question 1

You are developing a Snowpark application to analyze customer data'. You need to create a Snowpark DataFrame from a list of dictionaries, where each dictionary represents a customer with 'id', 'name', and 'city' keys. The data should be loaded efficiently. Consider these scenarios: 1 . The input data can sometimes contain missing values (e.g., a customer might not have a city specified). 2. You want to ensure optimal performance when loading the data, as the list can be very large. 3. You need the resulting DataFrame's schema to correctly infer the datatypes based on the input dictionary's values. Which of the following methods and considerations should be used to create a Snowpark DataFrame from a list of dictionaries to meet these requirements?

A. Use 'session.createDataFrame(data, and specify the 'nullable' property for each field within the schema. Additionally, define the data type explicitly. This optimizes performance.
B. Leverage Snowpark's optimized data loading by converting the list of dictionaries to a Pandas DataFrame first and then create a Snowpark DataFrame using 'session.createDataFrame(pandas_df)'. Pandas has optimized data loading.
C. Use 'session.createDataFrame(data)' and then explicitly cast columns with potential missing values to the correct datatype using method to ensure 'NULL' handling.
D. Use 'session.createDataFrame(data, where 'schema' is explicitly defined to handle missing data and ensure correct data types. This improves performance over schema inference.
E. Use 'session.createDataFrame(data)' with default settings. Snowflake will automatically infer the schema and handle missing values as 'NULL'.

Question 2

You are tasked with optimizing a Snowpark Python application that performs complex geospatial calculations on a large dataset. The application experiences significant performance bottlenecks due to the computational intensity of the geospatial functions. Which of the following strategies would be MOST effective in improving performance?

A. Utilize user-defined functions (UDFs) written in Java or Scala and leverage vectorized UDFs where possible.
B. Increase the size of the virtual warehouse to a larger instance (e.g., from X-SMALL to LARGE).
C. Rewrite the geospatial functions using native Python libraries within the Snowpark environment.
D. Distribute the dataset into smaller chunks using partitioning strategies within the Snowpark DataFrame API and process them independently.
E. Disable automatic query optimization features in Snowflake to gain more control over query execution.

Question 3

You are optimizing a Snowpark Python application that performs complex data transformations on a large dataset. You notice significant performance bottlenecks. Which of the following optimization techniques would be MOST effective in leveraging the Snowpark architecture to improve performance?

A. Manually partitioning the DataFrame into smaller chunks before applying transformations.
B. Using 'session.sql()' whenever possible instead of Snowpark DataFrame operations.
C. Using User-Defined Functions (UDFs) written in Python for all transformations, ensuring they are vectorized where possible, instead of native Snowpark functions.
D. Converting all Snowpark DataFrames to Pandas DataFrames before performing any transformations.
E. Exploiting lazy evaluation by chaining transformations together and avoiding unnecessary 'collect()' or 'toPandas()' calls.

Question 4

You have a Snowflake table 'PRODUCT CATALOG' with columns 'PRODUCT ID, 'PRODUCT NAME, and 'CATEGORY ID. You also have a table 'CATEGORY' with 'CATEGORY ID' and 'CATEGORY NAME. You need to create a Snowpark DataFrame that joins these two tables and includes only 'PRODUCT NAME and 'CATEGORY NAME. Assume a Snowpark Session object named 'session' is available. Which code snippet demonstrates creating the DataFrame using Snowpark's join functionality and column selection while using the 'table' method?

A.

B.

C.

D.

E.

Question 5

You are working with a Snowpark DataFrame 'products df' containing product information, including 'product_id', 'price', and 'discount'. You need to update the 'price' column in the 'products' table based on the following logic: If 'discount' is greater than 0.2, reduce the 'price' by 15%. If 'discount' is between 0.1 and 0.2 (inclusive), reduce the 'price' by 5%. Otherwise, keep the 'price' as is. Which of the following Snowpark code snippets efficiently implements this update? Assume 'products' table already exists and is correctly populated.

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

Solutions: