- Exam Code: SPS-C01
- Exam Name: Snowflake Certified SnowPro Specialty - Snowpark
- Certification Provider: Snowflake
- Corresponding Certification:Snowflake Certification
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Snowflake SPS-C01 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Testing, Debugging, and Deployment | - Production readiness
|
| DataFrame Operations and Data Processing | - Data transformation workflows
|
| Snowpark Fundamentals | - Snowpark architecture and concepts
|
| Performance Optimization and Best Practices | - Efficient Snowpark execution
|
| Data Engineering with Snowpark | - Pipeline development
|
| User Defined Functions and Stored Procedures | - Extending Snowpark with custom logic
|
Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:
Consider the following Snowpark Python code snippet:
- A. The code demonstrates the Snowpark architecture, where transformations are translated into SQL and executed in Snowflake's engine. Only the final 'collect()' brings the results back to the client.
- B. The 'upper()' function will be executed on the client-side (where the Python code is running) for each row in the 'customers' table.
- C.
- D. This code requires a configured Anaconda environment to run successfully.
- E. The function will retrieve all rows from the 'customers' table and store them in a local Pandas DataFrame before applying the function.
Correct Answer: A,C 🗳️
Explanation: Only visible for ITPassLeader members. You can sign-up / login (it's free).
You are developing a Snowpark application that processes large datasets stored in Snowflake. You need to implement custom User-Defined Functions (UDFs) written in Java. The UDF requires specific third-party libraries that are not available in the default Snowflake Java environment. What steps are necessary to package and deploy these UDFs correctly?
- A. Compile the Java code into a native library (e.g., a .so file), upload it to a Snowflake stage, and use the 'CREATE EXTERNAL FUNCTIONS command to invoke it.
- B. Create a separate Python UDF that imports the Java code using Jpype and then register the Python UDF with Snowflake
- C. Package the Java code and dependencies into a JAR file and upload it to a Snowflake stage. Use the 'CREATE FUNCTION' command with the 'IMPORTS' clause to reference the JAR file.
- D. Use the 'CREATE OR REPLACE JAVA FUNCTION' command directly in Snowsight and paste the Java code along with the dependencies' contents into the function body.
- E. Utilize the Snowpark API to create a 'snowpark.functions.udf object, including the Java code and dependencies. The Snowpark runtime will handle the deployment.
Correct Answer: C 🗳️
Explanation: Only visible for ITPassLeader members. You can sign-up / login (it's free).
Given a Snowpark DataFrame 'employees_df with columns 'employee_id' , 'department' , and 'salary' , and a second Snowpark DataFrame 'departments_df with columns 'department_id' and which of the following Snowpark code snippets correctly performs a join to retrieve employee information along with their department name, filtering for employees with salaries greater than $60,000, and then orders the result by department name?
- A.
- B.
- C.
- D.
- E.
Correct Answer: A 🗳️
Explanation: Only visible for ITPassLeader members. You can sign-up / login (it's free).
A Snowpark application frequently experiences performance degradation during peak hours. You suspect that resource contention on the virtual warehouse is the primary cause. Which of the following actions, when implemented together, would BEST address this issue and ensure consistent performance?
- A. Increase the AUTO_SUSPEND parameter of the virtual warehouse to a shorter duration to release resources more quickly.
- B. Implement workload management rules to prioritize Snowpark workloads during peak hours.
- C. Enable query acceleration for all Snowpark queries.
- D. Create separate virtual warehouses for different Snowpark workloads and apply resource monitors to limit credit consumption.
- E. Convert all Snowpark DataFrames to views to reduce computational overhead.
Correct Answer: B,D 🗳️
Explanation: Only visible for ITPassLeader members. You can sign-up / login (it's free).
You're tasked with creating a Snowpark UDF to calculate the Haversine distance between two sets of latitude and longitude coordinates (point A and point B). Which of the following statements about deploying and using this UDF is/are TRUE?
- A. The UDF can only be written in Python and must be deployed as an inline UDF within the Snowpark session.
- B. When defining the UDF with input types, the Python types must exactly match the corresponding Snowflake data types.
- C. The UDF, once defined, can be used inside of any DataFrame operation like 'select', 'filter' , and 'withColumn'
- D. The UDF can only be called directly from within the Snowpark session and cannot be used in standard Snowflake SQL queries.
- E. The UDF can be written in Python, Java, or Scala. Using a Java UDF will likely offer best performance, especially when dealing with very large datasets. You'll need to stage the compiled JAR file on an internal stage that Snowpark can access.
Correct Answer: C,D 🗳️
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