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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. A Snowpark application is designed to process data residing in a Snowflake table called 'ORDERS'. The application needs to create a temporary view named 'TEMP ORDERS VIEW based on a filtered subset of this table. The view should only be accessible within the current Snowpark session and should be automatically dropped when the session ends. What is the MOST efficient and correct Python code snippet using Snowpark to achieve this?

A)

B)

C)

D)

E)

2. You are developing a Snowpark Python UDF to perform sentiment analysis on product reviews. The UDF takes a text review (STRING) as input and returns a sentiment score (FLOAT). You want to operationalize this UDF, ensuring type safety and performance. Which of the following approaches is MOST recommended, considering both ease of use and explicit type declaration?

A) Using gudf(return_type=FloatType(), decorator with explicit data type registration.
B)

C) Casting the result of the UDF to FLOAT within the UDF definition.
D) Using only the function name without any explicit type declaration or registration.
E) Using Python type hints alone: 'def sentiment_score(review: str) -> float: .... and relying on Snowpark to infer types.

3. You have a Python function that performs complex data transformations, too intricate to express directly in Snowpark SQL. You want to register this as a User-Defined Table Function (UDTF) so that it can be used to expand rows in a Snowpark DataFrame. The UDTF takes two arguments: an ID (integer) and a string. It returns a table with three columns: (integer), (string), and 'timestamp' (timestamp). Which of the following code snippets correctly registers this UDTF, making it available for use within Snowpark?

A)

B)

C)

D)

E)

4. You have two Snowpark DataFrames, 'customers' and 'orders'. The 'customers' DataFrame has columns and 'customer name'. The 'orders' DataFrame has columns 'order id', 'customer id', and 'order amount'. You need to find all customers who have NOT placed any orders. Which of the following Snowpark set operations correctly implements this?

A)

B)

C)

D)

E)

5. You're designing a Snowpark application to process large CSV files containing sensor data'. Each CSV file has millions of rows, and you need to calculate aggregate statistics (e.g., average, min, max) for specific sensor readings. The processing needs to be highly parallelized for performance. You have the following code snippet (incomplete):

Which of the following code snippets, when inserted at the ' ??? Add code here to calculate aggregate statistics ??? ' marker, would correctly calculate the average, minimum, and maximum readings for a column named 'sensor value' and return the result in a new Snowpark DataFrame?

A)

B)

C)

D)

E)

Solutions: