Snowflake SPS-C01 : Snowflake Certified SnowPro Specialty - Snowpark

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are developing a Snowpark application that needs to connect to Snowflake using account identifiers. Your organization's Snowflake account is configured with federated authentication (Okta). Which of the following methods is the most secure and recommended way to establish a Snowpark session in this scenario, avoiding hardcoding credentials in your application and leveraging existing authentication mechanisms?

A) Create a dedicated Snowflake user with restricted permissions and use its username and password directly in the connection string.
B) Pass username and password directly in the connection properties along with the account identifier.
C) Store the username and password in environment variables and retrieve them in your Snowpark application to establish the session.
D) Use the connection parameter along with username and password directly in the connection properties.
E) Utilize Snowflake's support for OAuth and configure your application to acquire a token from Okta and use it to establish the Snowpark session using the 'authenticator parameter set to 'oauth'.

2. You are developing a Snowpark application in Python to perform sentiment analysis on customer reviews stored in a Snowflake table named 'CUSTOMER_REVIEWS. The table has columns 'REVIEW ONT), 'REVIEW TEXT (VARCHAR), and 'SENTIMENT SCORE (FLOAT). You want to define a UDF using Snowpark that leverages a pre-trained sentiment analysis model from the 'nltk' library (already uploaded to a stage). The UDF should take 'REVIEW TEXT' as input and return the sentiment score. Which of the following code snippets will correctly define and register the UDF, ensuring it's accessible for use in Snowpark DataFrames, taking into account potential serialization issues with 'nltk' models?

A)

B)

C)

D)

E)

3. You are working with a Snowpark DataFrame containing employee data, including columns 'employee_id', 'first_name', 'last_name', 'salary', and 'department'. You need to perform the following transformations: 1. Concatenate 'first_name' and into a new column called separating them with a space. 2. Increase each employee's salary by a percentage based on their 'department'. Department 'Sales' gets a 10% raise, 'Marketing' gets a 15% raise, and all other departments get a 5% raise. 3. Create a new column reflecting this raise. Which of the following Snowpark code snippets achieves these transformations correctly and efficiently? (Select all that apply)

A)

B)

C)

D)

E)

4. You have a Snowpark Python application that reads data from a Snowflake table, performs several transformations, and then writes the results back to a new Snowflake table. The transformations involve complex calculations and aggregations. During testing, you observe that the application is consuming a significant amount of credits. Which of the following optimization strategies would be MOST effective in reducing the credit consumption of your Snowpark application?

A) Disable auto-scaling on the Snowpark-optimized warehouse to prevent it from scaling up unnecessarily.
B) Minimize the amount of data transferred between Snowpark and Snowflake by pushing down transformations and using stored procedures where appropriate.
C) Use the 'cache()' method on intermediate Snowpark DataFrames to avoid recomputation of transformations.
D) Optimize the SQL queries generated by Snowpark by explicitly specifying join hints and using appropriate indexes.
E) Convert all Python User-Defined Functions (UDFs) to Java User-Defined Table Functions (UDTFs) for improved performance.

5. You have a Snowpark DataFrame containing sales data with columns 'sale_date', and 'sale_amount'. You need to calculate the cumulative sales amount for each product over time, ordered by 'sale_date'. Which of the following Snowpark code snippets correctly implements this using window functions?

A)

B)

C)

D)

E)

Solutions: