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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Objectives
Topic 1: DataFrame Operations and Data Processing	- Data transformation workflows 1. Joins and window functions 2. Filtering, selecting, and aggregations
Topic 2: Testing, Debugging, and Deployment	- Production readiness 1. Debugging Snowpark applications 2. Deployment strategies
Topic 3: Performance Optimization and Best Practices	- Efficient Snowpark execution 1. Resource utilization tuning 2. Pushdown optimization concepts
Topic 4: Snowpark Fundamentals	- Snowpark architecture and concepts 1. Snowpark APIs and supported languages 2. Snowflake execution model overview
Topic 5: User Defined Functions and Stored Procedures	- Extending Snowpark with custom logic 1. Stored procedures in Snowpark 2. Python UDFs
Topic 6: Data Engineering with Snowpark	- Pipeline development 1. Integration with Snowflake data pipelines 2. Batch processing workflows

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q147-Q152):

NEW QUESTION # 147
You have a Python function, 'calculate metrics(df: snowpark.DataFrame, metric name: str) -> snowpark.DataFrame', that calculates various metrics on a Snowpark DataFrame. You want to deploy this function as a stored procedure in Snowflake. You need to ensure that the stored procedure has appropriate permissions to read data from a table named 'customer data' and write results to a table named 'metrics_table'. Which of the following steps are necessary to achieve this, assuming you are using the 'session.sproc.register' method?

A. Grant the 'USAGE privilege on the database and schema containing the 'customer_data' and 'metrics_table' tables to the role executing the stored procedure.
B. When registering the stored procedure using 'session.sproc.register' , specify the argument and provide a 'replace=True' if necessary. This will allow you to assign ownership of the stored procedure to a role with the necessary privileges.
C. Specify the 'packages' argument in 'session.sproc.register' to include any Python dependencies required by the 'calculate_metrics' function.
D. Grant the 'SELECT privilege on the 'customer_data' table and the 'INSERT privilege on the 'metrics_table' table to the role executing the stored procedure.
E. Specify the 'imports' argument in 'session.sproc.register' with the list of packages which are needed to run 'calculate_metrics' function.

Answer: B,C,D

Explanation:
Options B, C, and D are correct. B is required to give the stored procedure permissions to read and write data to the appropriate tables. C: Making the SP permanent allows you to grant ownership to a specific role. D: The packages argument is essential for including any external Python dependencies needed by the function. Option A provides access to the database and schemas but does not grant access to the tables themselves. Option E provides a way to pass files that need to be used inside the function.

NEW QUESTION # 148
You are using Snowpark in Python within a Jupyter Notebook environment to analyze sales data'. You've established a connection to Snowflake and loaded your data into a Snowpark DataFrame named 'sales ff. You need to calculate the cumulative sales for each product category over time. The 'sales_df' DataFrame has columns 'SALE DATE' (DATE), 'PRODUCT CATEGORY' (VARCHAR), and 'SALE AMOUNT' (NUMBER). Which of the following approaches, or combination of approaches, will correctly calculate the cumulative sales while optimizing for Snowflake's performance and scalability? (Select all that apply)

A. Write a stored procedure in Snowflake that calculates the cumulative sales and call the stored procedure from your Snowpark application.
B. Use session.sql() to execute a SQL query with a window function for cumulative sales and load the data into snowpark DataFrame.
C. Use the and 'sort()' functions on the Snowpark DataFrame to order data by 'PRODUCT CATEGORY' and 'SALE DATE before calculating the cumulative sum using a UDF.
D. Use a Window function within a Snowpark DataFrame transformation to calculate the cumulative sum for each product category, ordered by 'SALE_DATE.
E. Iterate through the rows of the 'sales_df DataFrame in the Jupyter Notebook and manually calculate the cumulative sales using Python code.

Answer: A,B,D

Explanation:
Options A, C, and E are correct. Option A utilizes Snowpark's DataFrame transformations with window functions, which are optimized for Snowflake's engine and can efficiently handle large datasets. This leverages Snowflake's parallel processing capabilities. Option C involves creating a stored procedure within Snowflake. This approach pushes the computation to the Snowflake server, potentially improving performance, especially if the cumulative sales calculation is complex. This also benefits from Snowflake's optimization capabilities. Option E executes a SQL query with a window function that's optimized. Option B is incorrect because iterating through rows in a Jupyter Notebook will be extremely slow and inefficient for large datasets. It defeats the purpose of using Snowpark for distributed data processing. Calculating cumulative sales this way doesn't leverage Snowflake's capabilities. Option D is partially correct in that it orders the data, but using a UDF for cumulative sum calculation will likely be less efficient than using native window functions within Snowflake.

NEW QUESTION # 149
You have developed a Python function that performs complex data transformation on customer data'. You want to operationalize this function as a UDTF in Snowpark to process large datasets efficiently. The function takes a customer ID and a list of transaction amounts as input and returns a table with calculated risk scores for each transaction. Which of the following code snippets correctly defines and registers this UDTF in Snowpark, ensuring proper type handling and scalability?

A.
B.
C. Options B and C are the most correct.
D.
E.

Answer: C

Explanation:
Options B and C correctly defines the UDTF.Option B uses return_type and option C uses output_schema, both work. Option A is incorrect because a UDTF needs to be defined as a class with a 'process' method that yields rows, not as a function that returns a list. Option D uses an incorrect way to specify ArrayType ('array') . The other options are either syntactically incorrect or do not follow the correct UDTF definition pattern.

NEW QUESTION # 150
You have a Snowpark Python application that performs several data transformations. You need to implement error handling to catch exceptions during DataFrame operations and log them to a Snowflake table named 'error _ log'. You also need to ensure that if any exception occurs, the application continues to run and processes the remaining data. Which of the following approaches (or combination of approaches) would be MOST suitable?

A. Use Snowpark's logging API to automatically capture and log all errors without needing explicit try-except blocks. (Assume such an API exists, even if it doesn't in the current Snowpark version.)
B. Use a global try-except block to catch all exceptions and log them to the 'error_log' table.
C. Wrap individual DataFrame operations (e.g., 'filter', 'select, 'groupBy') in try-except blocks to catch exceptions specific to those operations and log them.
D. Implement a custom User-Defined Function (UDF) that encapsulates the error-prone logic. Within the UDF, use try-except blocks to handle errors and return error messages along with the processed data. Add a column in Dataframe that contains the errors. Then, create table for the Dataframe using snowpark's API
E. Rely solely on Snowflake's built-in error handling mechanisms, as Snowpark automatically handles all exceptions.

Answer: C,D

Explanation:
Options B and C, when combined, provide the most robust error handling. Wrapping individual DataFrame operations in try-except blocks (B) allows you to catch specific exceptions and log them appropriately, preventing a single error from crashing the entire application. Implementing a UDF with error handling (C) encapsulates complex logic and allows you to return both processed data and error messages, making it easier to identify and address issues. A global try-except block (A) might catch errors, but it doesn't provide granular control or context. Relying solely on Snowflake's built-in mechanisms (D) is insufficient for custom error logging and continuing execution. While a Snowpark logging API (E) would be ideal, such an API doesn't currently exist and relies on an assumption.

NEW QUESTION # 151
You have a Snowpark DataFrame named and want to create a stored procedure that calculates the average purchase amount for each customer. The stored procedure should accept the DataFrame as input, perform the aggregation, and return a new DataFrame with the results. Which of the following code snippets BEST demonstrates how to correctly define and deploy this stored procedure?

Answer: E

Explanation:
Option E is the most correct. It correctly registers the stored procedure using 'session.sproc.register' with the correct return type annotation for a DataFrame (using TableType and StructFields) and explicitly declares the input type to be DataFrame using 'input_types' parameter in registration. Option A uses the '@sproc' decorator which is the older API (still valid, but less explicit). Option B uses session.sql which is not appropriate for creating DataFrame based stored procedures. Option C is not correct because the return type SeqType(FloatType()) expects a sequence of float types instead of a DataFrame structure. Option D writes the result to a table, which is not the defined requirement to return a DataFrame.

NEW QUESTION # 152
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