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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are tasked with building a Snowpark application to perform sentiment analysis on customer reviews stored in a Snowflake table named 'CUSTOMER REVIEWS'. The application should be deployed as a UDF. The sentiment analysis is performed by a third-party Python library, 'sentiment_analyzer'. Due to security constraints, direct internet access is prohibited from within the Snowflake environment. What steps are necessary to ensure the 'sentiment_analyzer' library can be used by your Snowpark UDF?

A) Use the 'packages' parameter in the UDF creation statement to specify the 'sentiment_analyzer' library from Anaconda.
B) Package the 'sentiment_analyzer' library into a ZIP file and upload it to a Snowflake stage, then specify the ZIP in the 'imports' parameter of the UDF creation statement.
C) Install the 'sentiment_analyzer' library using 'conda install' directly within the Snowpark session before creating the UDF.
D) Request Snowflake support to whitelist the 'sentiment_analyzer' library for direct download during UDF execution.
E) Package the 'sentiment_analyzer' library into a JAR file and upload it to a Snowflake stage, then specify the JAR in the 'imports' parameter of the UDF creation statement.

2. 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)

3. You have a Snowpark DataFrame 'df sales' containing sales data with columns like 'order id', 'product id', 'quantity', and 'sale_price' You want to persist this data into a Snowflake table named "SALES DATA'. You also want to create a dynamic table on top of this base table for faster analytics. You need to choose the appropriate persistence strategy and consider the implications of using a dynamic table. Which of the following options represents the BEST approach?

A) Option A
B) Option E
C) Option B
D) Option C
E) Option D

4. You are building a Snowpark application that requires you to connect to Snowflake from an environment where directly specifying credentials in the code is not permitted for security reasons. Which of the following are valid and recommended ways to securely pass authentication information to the Snowpark Session?

A) Storing credentials in a Snowflake stage and retrieving them from there at runtime. This is an acceptable, though more complex, solution.
B) Using the Snowflake CLI's 'snowflake configure' command and relying on the A.snowflake/config' file. This is suitable for development but not recommended for production due to local file dependency.
C) Storing credentials in a dedicated secret management service (e.g., HashiCorp Vault, AWS Secrets Manager) and retrieving them using an appropriate API. This is the most secure and recommended approach for production environments.
D) Using environment variables and retrieving them using 'os.environ' to build the connection parameters. This is a secure and recommended approach.
E) Hardcoding the credentials in the Snowpark Python script and obfuscating them using Base64 encoding. This provides security by obscurity, making it a reasonably secure approach.

5. 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)

Solutions: