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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Transformations and DataFrame Operations	35%	- Filtering, Aggregating, and Joining DataFrames - Using built-in functions - Complex data pipelines - Persisting transformed data - Window functions
Topic 2: Snowpark Concepts	15%	- Stored procedures and conditional logic - Snowpark architecture and core concepts - Snowpark DataFrames and query plans - Snowpark Sessions and connection management - Client-side vs. Server-side execution - Transformations vs. Actions
Topic 3: Performance Optimization and Best Practices	20%	- Caching strategies - Debugging and explain plans - Minimizing data transfer - Query pushdown and optimization - Warehouse sizing for Snowpark - Vectorized UDFs
Topic 4: Snowpark API for Python	30%	- Reading and writing data - Working with Semi-structured data - User-Defined Functions (UDFs) and Stored Procedures - DataFrame creation and manipulation - Establishing connections and session management

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q269-Q274):

NEW QUESTION # 269
A UDTF (User-Defined Table Function) named 'split_sentences" takes a text string as input and returns a table with each row containing a single sentence from the input. You need to grant SELECT privilege on this UDTF to a specific role, 'DATA ANALYST'. Which of the following SQL statements will achieve this?

A. GRANT USAGE ON FUNCTION TO ROLE DATA_ANALYST;
B. GRANT SELECT ON TABLE FUNCTION TO ROLE DATA_ANALYST;
C. GRANT OWNERSHIP ON FUNCTION TO ROLE DATA ANALYST,
D. GRANT EXECUTE ON FUNCTION TO ROLE DATA_ANALYST;
E. GRANT ALL PRIVILEGES ON FUNCTION TO ROLE DATA_ANALYST;

Answer: B

Explanation:
Since UDTFs return a table, you need to grant the 'SELECT privilege on the table function to allow the role to query the results produced by the UDTF. The syntax is 'GRANT SELECT ON TABLE FUNCTION'. option A is wrong it's for UDE Options C and E provide too many privileges, potentially allowing unintended modifications. Option D is not the correct Privilege.

NEW QUESTION # 270
When creating UDFs/UDTFs in Snowpark Python, what are the advantages of explicitly specifying data types (either via Python type hints or the registration API) compared to relying on implicit type inference?

A. Improved performance due to reduced overhead in data type resolution at runtime.
B. Reduced deployment time.
C. Automatic data type conversion by Snowflake, eliminating the need for explicit casting within the UDF/UDTF.
D. Early detection of type-related errors during development, preventing runtime failures.
E. Enhanced code readability and maintainability, making it easier to understand the expected data types.

Answer: A,D,E

Explanation:
Specifying data types explicitly offers several benefits. (A) Explicit data types allow Snowflake to optimize query execution by eliminating the need to infer types at runtime, resulting in improved performance. (B) Type hints and registration APIs enhance code readability and maintainability by clearly indicating the expected data types. (C) Explicit data types enable early detection of type-related errors during development, preventing unexpected runtime failures. (D) While Snowflake can perform some implicit conversions, explicit type declarations don't guarantee automatic conversion in all scenarios and manual casting might still be needed. (E) deployment time is not significantly affected.

NEW QUESTION # 271
You are working with a data science team that needs to create Snowpark DataFrames from various file types (CSV, JSON, Parquet, and XML) stored in different locations (internal stages, external stages on AWS S3, and Azure Blob Storage). The team wants a unified and reusable function to create DataFrames, abstracting away the specific file format and location details. Which of the following approaches using Snowpark Python API will provide the MOST flexible and maintainable solution?

A. Create separate functions for each file type and location combination (e.g.,
B. Create a class hierarchy with an abstract base class 'DataFrameReader' that defines a 'read_file' method. Implement subclasses for each file format and location, overriding the 'read_file' method with the specific logic for that format and location.
C. Create a generic function str, file_format: str, options: dicty that uses 'getattr(session.read, file format)' to dynamically call the appropriate 'session.read' method based on the 'file_format' parameter. Pass additional configuration through the 'options' dictionary.
D. Implement a single function that uses a series of 'if/elif/else' statements to determine the file type and location, then calls the appropriate 'session.read' method with the corresponding options.
E. Use the 'session.sqr method with dynamically generated SQL queries that include the file format and location details. Construct the SQL query string based on the input parameters.

Answer: C

Explanation:
Option C provides the best balance of flexibility, maintainability, and conciseness. Using 'getattr(session.read, file_format)' allows dynamically calling the appropriate 'session.read' method (e.g., 'session.read.csv', 'session.read.json') based on a string parameter. Passing additional configuration through a dictionary allows customizing the read operation without modifying the core function. Options A, B, D, and E are less flexible, more verbose, or less efficient.

NEW QUESTION # 272
A data engineering team is developing a Snowpark stored procedure to perform complex data transformations and load the results into a target table. They want to operationalize this procedure by scheduling it to run daily. Which of the following is the MOST reliable and scalable way to schedule the execution of this Snowpark stored procedure within Snowflake?

A. Create a Python script that uses the Snowpark API to connect to Snowflake and execute the stored procedure, then schedule the script using a Linux cron job.
B. Utilize a third-party orchestration tool, such as Airflow, to schedule and monitor the execution of the stored procedure through the Snowflake connector.
C. Use Snowflake Pipes to ingest data and trigger the stored procedure based on new data arrival.
D. Implement a Streamlit application that calls the stored procedure when a button is pressed.
E. Use Snowflake Tasks to schedule a SQL statement that calls the stored procedure.

Answer: E

Explanation:
Snowflake Tasks are the recommended way to schedule stored procedures within Snowflake. They are a native Snowflake feature, providing scalability, reliability, and integration with Snowflake's monitoring and management tools. Airflow is a valid option, but adds external dependencies.

NEW QUESTION # 273
You have created a Snowpark stored procedure in Python that accesses a Snowflake stage to read configuration files. To enhance security, you want to grant the stored procedure specific permissions to only read files from that stage, without granting broader account- level access. Which of the following approaches is the MOST secure and granular way to achieve this?

A. Grant the 'USAGE privilege on the stage to the role that owns the stored procedure.
B. Create a stored procedure with 'EXECUTE AS CALLER and ensure the caller (user executing the procedure) has the necessary privileges.
C. Grant the 'SELECT privilege on the stage to the role that owns the stored procedure.
D. Grant the 'READ privilege directly to the stored procedure using the 'ALTER PROCEDURE command.
E. Create a Snowflake custom role and grant the 'READ' privilege on the stage to this role, then grant this role to the stored procedure's owner.

Answer: B

Explanation:
Using 'EXECUTE AS CALLER ensures the stored procedure executes with the privileges of the user calling it. This is the most secure and granular approach because you don't need to grant any specific privileges to the stored procedure itself. The user calling it must already have the necessary permissions to access the stage.

NEW QUESTION # 274
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