Comprehensive Snowflake SPS-C01 Questions in PDF Format

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q265-Q270):

NEW QUESTION # 265
You have a Snowflake stage containing image files. You need to write a Snowpark Python application that extracts metadata (e.g., image resolution, format) from these images and stores the metadata in a Snowflake table. You want to leverage a Python library, such as Pillow (PIL), for image processing. Which of the following steps are necessary to correctly and efficiently implement this?

Answer: D

Explanation:
Option B is the MOST correct. Using a Conda environment specification file ('environment.yml') and 'session.add_packages' is the recommended way to manage dependencies in Snowpark. It ensures that the correct version of Pillow is available and simplifies the deployment process. Option A is an older method and may not be as reliable. Option C involves UDFs which, while valid, can be less efficient than using native Snowpark functionalities directly. Option D is incorrect, Snowflake doesn't have built-in image processing functions. Option E again defeats the purpose of server-side processing.


NEW QUESTION # 266
You need to perform a set difference operation between two DataFrames in Snowpark Python. 'dfl' contains customer IDs from a marketing campaign, and 'df2 contains customer IDs from a recent purchase event. You want to identify customers who were targeted in the campaign but did not make a recent purchase. Both DataFrames have a column named 'customer id'. Which of the following approaches provides the most efficient way to accomplish this task in Snowpark?

Answer: A

Explanation:
Option C, using a 'left_anti' join, is the most efficient way to perform a set difference operation between two DataFrames in Snowpark. A join returns only the rows from the left DataFrame Cdfl s) where the join condition is not met in the right DataFrame Cdf2). This leverages Snowflake's query optimizer for optimal performance. Option A, 'subtract(df2)' , is equivalent to 'exceptAll(df2)' (Option B) and removes duplicate rows. While functionally correct, join is often more performant, especially for larger datasets. Option D is highly inefficient as it collects the 'customer_id' from 'df2 to the driver, it should be avoided. Option E calculates intersection, not difference.


NEW QUESTION # 267
Consider the following Snowpark Python code snippet designed to create a DataFrame and then register a custom function (UDF):

This code runs successfully. However, you need to deploy this as a stored procedure. What minimal changes are required to make this code runnable as a Snowpark Python stored procedure and callable from SQL?

Answer: A

Explanation:
When deploying as a stored procedure, 'return will cause an error since stored procedures cannot return Snowpark DataFrames directly to SQL. You need to return the DataFrame to pandas using 'return and remove 'return_type' and 'input_types' arguments of udf to allow inference.


NEW QUESTION # 268
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)

Answer: A,B,C

Explanation:
Options A, B and D provide valid ways to fetch 'discount_rate' as a single numerical value. And fetch the data and gets the first value from the first row. Similarly, gets the data and return the first row. However, Option C does not have LIMIT 1 and will not work. Option E fetches one row as one array, thus requires rate[0] to compute discounted_price.


NEW QUESTION # 269
You are working with a Snowpark DataFrame named containing information about products, including 'CATEGORY , 'SUBCATEGORY , and 'PRICE'. You want to determine the maximum price for each subcategory within each category. Furthermore, you need to filter the results to only include categories that have more than 5 subcategories. Which of the following Snowpark Python code snippets accomplishes this task? (Select all that apply)

Answer: B,E

Explanation:
The correct options are D and E. They both address the requirement and count distinct subcategories within each category. Option D: Correctly calculates the maximum price for each subcategory within each category. Then separately calculates the count of DISTINCT subcategories for each category. Filters the categories to include only those with more than 5 subcategories. Joins the two resulting DataFrames to provide the final output. This is a standard and explicit way to accomplish the task. Using countDistinct to ensure each subcategory is only counted once. Option E: Aggregates Max Price and CountDistinct of subcategory into same dataframe, this addresses all requirements in a more concise manner. Option A: Does not give the right result since 'count' will not give the countDistinct. Also using 'COUNT(SUBCATEGORYV in category_countS dataframe results in count of all rows in the group instead of the number of groups (number of Subcategories). Option B: Window functions is irrelevant here, and requires more coding while can be handled efficiently by aggregating and countDistinct combination Option C: Option C doesn't aggregate maximum price for each subcategory within each category as the question mentions.


NEW QUESTION # 270
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