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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Snowpark API and Development	30%	- Python API fundamentals 1. Column operations and functions 2. Data persistence and writing results 3. DataFrame creation from tables, views, SQL - Multi-language support 1. Java and Scala API basics 2. Environment setup and dependencies
Snowpark Concepts and Architecture	25%	- Snowpark architecture and execution model 1. Client-side vs server-side processing 2. Lazy evaluation and DAG execution 3. Transformations vs actions - Session management and connection 1. Authentication and connection settings 2. Create and configure Snowpark sessions
Data Transformations and Operations	35%	- User-defined logic 1. UDFs, UDAFs, UDTFs 2. Stored procedures with Snowpark - DataFrame manipulation 1. Filtering, sorting, grouping, aggregation 2. Selection, projection, renaming, casting 3. Joins, unions, set operations - Advanced operations 1. Pivot and unpivot transformations 2. Semi-structured data processing 3. Window functions and analytics
Performance and Best Practices	10%	- Optimization techniques 1. Query pushdown and execution plans 2. Minimizing data movement 3. Caching and warehouse sizing - Security and governance 1. Data protection and compliance 2. Access control and permissions

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Snowflake Certified SnowPro Specialty - Snowpark SPS-C01 Prüfungsfragen mit Lösungen (Q181-Q186):

181. Frage
You have a Snowflake table containing JSON data with nested arrays and objects representing website user interactions. You want to extract all 'product_id' values from within an array named 'viewed _ products' nested inside a 'session' object for each event, using Snowpark for Python. Assume the 'raw_events' table has a variant column called 'event_data". Which of the following Snowpark code snippets will correctly extract and flatten the 'product_id' values into a DataFrame?

Antwort: C

Begründung:
Option A correctly uses the 'flatten' function to unnest the 'viewed_products' array. It then accesses the 'product_id' within each flattened element using Tvalue']['product_idT. Options B, C, D and E either uses wrong function explode or does not handle nested structure properly or converts to varchar prematurely.

182. Frage
You are developing a Snowpark Python application that reads data from an external stage (AWS S3) and performs several transformations before loading it into a Snowflake table. During testing, you encounter the following error: net.snowflake.client.jdbc.SnowflakeSQLException: SQL compilation error: User does not have OWNERSHIP privilege on integration object 'YOUR INTEGRATION NAME". You have confirmed that the user has the 'USAGE privilege on the integration. Besides granting ownership, which of the following actions could resolve this issue in the MOST secure and efficient way?

A. Use the 'COPY INTO' command with the 'ON_ERROR = CONTINUE option to bypass the permission error.
B. Create a custom role with the 'USAGE privilege on the integration and the 'CREATE TABLE' privilege on the database. Grant this role to the user.
C. Grant the 'CREATE TABLE privilege to the user on the database where the target table resides.
D. Modify the integration to include a service principal that the Snowpark application will use to authenticate, eliminating the need for user-level privileges.
E. Grant the ' READ privilege on the external stage to the user.

Antwort: B

Begründung:
Option B is the MOST secure and efficient. The error indicates that the user lacks necessary privileges to utilize the integration for creating objects (in this case, likely an internal stage used during the transformation process). Granting a custom role with both 'USAGE on the integration and 'CREATE TABLE on the database adheres to the principle of least privilege. Option A grants broad privileges to the user, which is less secure. Option C involves complex integration setup and might not be necessary for a simple data loading scenario. Option D is related to reading data from the external stage, not using the integration for internal operations. Option E bypasses the error without addressing the underlying permission issue.

183. Frage
You are working with a Snowpark DataFrame containing customer data'. One of the columns, 'phone number', contains phone numbers in various formats (e.g., '123-456-7890', '(123) 456-7890', '1234567890'). You need to standardize all phone numbers to the format '+1-123-456-7890' using Snowpark for Python. You also want to handle cases where the phone number is NULL gracefully, replacing them with '+1-000-000-0000'. Which of the following Snowpark code snippets is the most efficient and correct way to achieve this?

Antwort: A

Begründung:
Option C is the most efficient because it uses built-in Snowpark functions (when, regexp_replace, substring, concat, length, and lit) to perform the transformation directly on the server-side. It first handles NULL values. It then removes non-numeric characters. Finally, it checks the length of the remaining digits before formatting, ensuring only valid 10-digit numbers are transformed, setting others to NULL. Options A, D, and E do not handle the case where after removing non-numeric characters, the length of phone number is not 10. Option B uses a UDF, which is generally less efficient than using built-in functions as it involves serialization/deserialization overhead .

184. Frage
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. 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.
B. Utilize the Snowpark API to create a 'snowpark.functions.udf object, including the Java code and dependencies. The Snowpark runtime will handle the deployment.
C. 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.
D. 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.
E. Create a separate Python UDF that imports the Java code using Jpype and then register the Python UDF with Snowflake

Antwort: A

Begründung:
The correct approach involves packaging the Java code and its dependencies into a JAR file, uploading it to a Snowflake stage, and using the 'CREATE FUNCTION' command with the 'IMPORTS clause. This informs Snowflake to include the specified JAR file in the classpath when executing the UDF. Other options are not valid ways to deploy Java UDFs with dependencies in Snowflake.

185. Frage
You have a Snowpark DataFrame with columns 'order_id', 'product_id', 'sale_date' (DATE), and 'sale_amount'. You need to perform the following transformations: 1. Filter out sales records before January 1, 2023.2. Group the data by 'product_id' and calculate the total 'sale_amount' for each product. 3. Create a new column 'average_sale_amount' by dividing the total 'sale_amount' by the number of distinct 'order_id' for each product. You must alias the aggregate function. Which of the following Snowpark code snippets correctly implements these transformations?

Antwort: B

Begründung:
Option E is the most appropriate and correct. It uses for explicit date conversion, ensures correct filtering, aggregates with aliases, and calculates the 'average_sale_amount' correctly using the aliased columns. Options A, B, C and D are all valid for Snowflake. Option B incorrectly passes the date string. 'as_' instead of 'alias' are not valid and will cause an issue.

186. Frage
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