Snowflake Certified SnowPro Specialty - Snowpark - SPS-C01 Exam Practice Test

You've developed a Snowpark Python UDTF that performs complex data transformation. This UDTF needs to be operationalized within a data pipeline. You want to ensure high performance and scalability. Which of the following strategies will be MOST effective in operationalizing this UDTF?

A Snowpark developer is using to create a Snowpark session. They want to ensure that the session uses a specific role and warehouse, but only if those parameters are not already defined in the Snowflake CLI configuration. Which of the following code snippets correctly implements this behavior?

A data scientist is developing a Snowpark application that needs to authenticate to Snowflake using Key Pair Authentication. Which of the following steps are essential for configuring the Snowflake CLI to enable Key Pair Authentication and then correctly create a Snowpark session? (Select TWO)

You have a Snowpark Python stored procedure that needs to access environment variables stored securely within Snowflake. Which of the following code snippets demonstrates the correct way to retrieve the value of an environment variable named 'API KEY' within your stored procedure?

Consider the following Snowpark code snippet that defines and registers a UDF:

Which of the following statements about this code are TRUE?

You are developing a Snowpark application to ingest a large dataset into Snowflake. You have a DataFrame with a schema that matches the target table 'TARGET TABLE. Due to network constraints, you need to optimize the insertion process to minimize the number of API calls. Which of the following approaches would provide the MOST efficient way to insert the data?

You have developed a Snowpark application that uses a Python UDF to perform sentiment analysis on text data extracted from JSON files stored in a Snowflake stage. The UDF relies on a large pre-trained machine learning model that is loaded during the UDF initialization. After deploying the application, you observe that the UDF initialization is taking a significant amount of time, causing slow query performance. What are the three MOST effective strategies to optimize the UDF initialization time in this scenario?

A data engineer wants to create a Snowpark session using environment variables defined in a .env' file. The file contains the following: SNOWFLAKE ACCOUNT=myaccount.snowflakecomputing.com SNOWFLAKE USER=snowpark_user SNOWFLAKE SNOWFLAKE DATABASE=mydb SNOWFLAKE SCHEMA=myschema SNOWFLAKE WAREHOUSE=mywarehouse Which code snippet correctly establishes a Snowpark session using these environment variables?

You have a complex Snowpark Python UDF that aggregates data from various sources and returns a dictionary containing several metrics (e.g., '{'average price': 12.50, 'total sales': 1000, 'customer count': 50}'). You need to operationalize this UDF and ensure proper data type handling for each metric. Which of the following is the MOST appropriate way to define the return type using the registration API?

Consider a scenario where you have a table 'EMPLOYEES' with columns 'employee id', 'department', and 'salary'. You want to delete employees who belong to either the 'HR' or 'Finance' department and have a salary less than 60000. Which of the following Snowpark DataFrame operations correctly implements this deletion?

You are profiling a Snowpark application that uses a combination of SQL queries and Python UDFs. You observe that a particular stage involving a UDF is taking significantly longer than expected. You suspect that the UDF's performance is the bottleneck. Which of the following steps would be the MOST comprehensive approach to diagnose and address the performance issue?

You are working with Snowpark and need to persist the results of a DataFrame 'df to a Snowflake stage named 'my_stage'. You want to achieve the following: 1. Write the data in JSON format. 2. Use snappy compression. 3. Handle potential write errors gracefully. 4. Overwrite any existing files with the same name. Which of the following approaches can achieve these requirements? (Select all that apply)

A data engineering team is developing a Snowpark application to process large volumes of data'. They aim to leverage session parameters for fine-grained control over query execution and resource allocation. Which of the following methods is the MOST efficient and secure way to set session parameters, ensuring that sensitive information like warehouse size and query timeouts are dynamically adjusted based on the workload without hardcoding values in the application?

You are tasked with optimizing a Snowpark Python stored procedure that performs complex data transformations on a DataFrame. The procedure frequently encounters out-of-memory errors when processing large datasets. Which of the following strategies could you implement to mitigate these memory issues within the stored procedure's code ? Choose all that apply.