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Snowflake SPS-C01 Exam Syllabus Topics:

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

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Snowflake Certified SnowPro Specialty - Snowpark SPS-C01 Prüfungsfragen mit Lösungen (Q261-Q266):

261. Frage
You are developing a Snowpark application that needs to read data from a set of CSV files stored in a Snowflake stage named ' my_stage'. The files have a header row and are comma-delimited. You want to use the Snowpark API to create a DataFrame from these files, automatically inferring the schema. Which of the following code snippets correctly achieves this?

Antwort: E

Begründung:
Option D is the correct way to read CSV files from a stage in Snowpark with header and schema inference using .format("csv").option(...y. Option A is close, but it doesn't use the correct syntax for specifying the stage location; it expects a file path relative to the current working directory.Option B incorrectly uses .csv()' with directly.Option C correctly passes options, but must use the load method in conjunction with format option. Option E has incorrect path specification in addition to incorrect function chain. Option D uses .load()" after setting the format and options is most appropriate.

262. Frage
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. 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.
B. Grant the 'USAGE privilege on the database and schema containing the 'customer_data' and 'metrics_table' tables to the role executing the stored procedure.
C. Specify the 'imports' argument in 'session.sproc.register' with the list of packages which are needed to run '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 'packages' argument in 'session.sproc.register' to include any Python dependencies required by the 'calculate_metrics' function.

Antwort: A,D,E

Begründung:
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.

263. Frage
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. Use a Window function within a Snowpark DataFrame transformation to calculate the cumulative sum for each product category, ordered by 'SALE_DATE.
B. Write a stored procedure in Snowflake that calculates the cumulative sales and call the stored procedure from your Snowpark application.
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 session.sql() to execute a SQL query with a window function for cumulative sales and load the data into snowpark DataFrame.
E. Iterate through the rows of the 'sales_df DataFrame in the Jupyter Notebook and manually calculate the cumulative sales using Python code.

Antwort: A,B,D

Begründung:
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.

264. Frage
You've transformed a large Snowpark DataFrame and want to persist it to a Snowflake stage for downstream applications. Your requirements are: 1. The data must be written in CSV format. 2. The files must be GZIP compressed. 3. A header row should be included in each file. 4. The files should be stored in a stage named 'customer_stage' in your Snowflake database. Which of the following code snippets correctly implements this, ensuring optimal performance and resource utilization?

A. Option C
B. Option B
C. Option E
D. Option D
E. Option A

Antwort: B

Begründung:
Option B provides the most concise and readable way to achieve the desired outcome using the dedicated writer method. It directly specifies the header and compression options as parameters. Options A, D, and E require specifying the file format separately using 'format('csv')' and configuring header and compression through options, which is less direct. Option C has incorrect order - it need to set format first before setting options, so its less readable.

265. Frage
You are developing a Snowpark application that utilizes a UDF. You need to ensure that the UDF runs with the privileges of the caller (the user executing the query). Which of the following steps are necessary to accomplish this while creating the Snowpark session?

A. When creating the Snowpark session, explicitly set the 'privilege' parameter to 'CALLER.
B. No special steps are required when creating the Snowpark session; the UDF automatically inherits the caller's privileges.
C. When defining the UDF using Snowpark, ensure the argument is passed in the decorator. Create the Snowpark session as usual.
D. The account administrator needs to explicitly grant the 'CREATE FUNCTION' privilege to the user.
E. After creating the Snowpark session, execute the SQL command 'ALTER SESSION SET

Antwort: C

Begründung:
To ensure a UDF runs with the privileges of the caller, you need to explicitly specify the 'api_caller_identity=sf.Caller.CALLER when defining the UDF using Snowpark. This instructs Snowflake to execute the UDF with the caller's permissions. No special session configurations are needed. Option A is irrelevant for caller's identity. Option B is incorrect as it's not automatic. Option D does not exist. Option E is a valid SQL command but needs to be implemented in Python using 'session.sqr function

266. Frage
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