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Snowflake SPS-C01 Exam Syllabus Topics:

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

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q221-Q226):

NEW QUESTION # 221
A data engineering team is developing a Snowpark application that processes large volumes of JSON data'. They have created a UDF using Python that parses JSON strings and extracts specific fields. They need to deploy this UDF and ensure it can handle malformed JSON without causing the entire Snowpark job to fail. Which of the following strategies BEST addresses both the deployment and error handling requirements?

A. Create a Python UDF using the 'snowflake-snowpark-python' library and register it in Snowflake. Implement error handling using Snowflake's built-in 'ERROR HANDLING' clause in the 'CREATE FUNCTION' statement. Package the Python code as a ZIP file and upload it to a stage, using the 'imports' clause in the UDF definition.
B. Create an external function pointing to an AWS Lambda function that handles the JSON parsing. Configure the Lambda function to retry on failure, using SNS for notifications. No need to use ZIP file or any 'imports' clause.
C. Create a Java UDF instead of Python. Java has better JSON parsing libraries. Upload the JAR file to a stage and register the UDF with 'imports' clause referencing the JAR file. Use try-catch for error handling.
D. Create a Python UDF using the 'snowflake-snowpark-python' library and register it in Snowflake. Implement error handling using 'try-except block within the UDF to catch 'json.JSONDecodeError' exceptions and return NULL. Package the Python code as a ZIP file containing any necessary dependencies and upload it to a stage, using the 'imports' clause in the UDF definition.
E. Create a Python UDF and register it in Snowflake, using 'try-except' block within the UDF to catch 'json.JSONDecodeError' exceptions. Package the Python code with the 'snowflake-snowpark-python' library.

Answer: D

Explanation:
Option E is the best approach. Python UDFs are well-suited for JSON parsing, especially when using libraries like 'json' . The 'try- except' block provides robust error handling for malformed JSON, ensuring that errors are gracefully handled by returning NULL, preventing the entire job from failing. Packaging the Python code, including dependencies in a ZIP file uploaded to a stage, and using the 'imports' clause ensures that the UDF has access to all necessary resources. Options A, B, C and D do not provide the most suitable combination of error handling and dependency management or aren't the recommended/performant approach. Using external functions (D) introduces extra latency and cost.

NEW QUESTION # 222
You have a Snowflake table 'PRODUCT CATALOG' with columns 'PRODUCT ID, 'PRODUCT NAME, and 'CATEGORY ID. You also have a table 'CATEGORY' with 'CATEGORY ID' and 'CATEGORY NAME. You need to create a Snowpark DataFrame that joins these two tables and includes only 'PRODUCT NAME and 'CATEGORY NAME. Assume a Snowpark Session object named 'session' is available. Which code snippet demonstrates creating the DataFrame using Snowpark's join functionality and column selection while using the 'table' method?

Answer: C

Explanation:
Option D correctly joins the tables using the 'join' method with a column expression specifying the join condition. It also correctly selects only the desired columns using and option A incorrectly uses 'PRODUCT_ID instead of for the join and needs 'col()' to reference columns. option B selects 'PRODUCT_NAME', 'CATEGORY_NAME' without using 'col(Y which will cause an error since the join brings duplicate column names from product_df and category_df. Option C uses Pythonic '[['PRODUCT_NAME', 'CATEGORY_NAME'I]' which only works on Pandas dataframes. option E attempts to use col() to reference the columns in the join condition which is incorrect as it is a column expression needs to use fully qualified table name for the same named column in two dataframes; moreover selection is missing col().

NEW QUESTION # 223
You are working with image files stored in a Snowflake internal stage named 'image_stage'. You need to write a Snowpark Python application to resize these images using a Python library called 'PIG. The resizing logic is encapsulated in a function called resize_image(snowflake_file: SnowflakeFile, width: int, height: int) -> bytes. Which of the following code snippets correctly registers he 'resize image' function as a UDF and applies it to the image files?

Answer: C

Explanation:
Option E correctly implements the image resizing using Snowpark UDF and 'SnowflakeFile' object. - The 'resize_image' function takes the as input and constructs the SnowflakeFile object correctly - 'SnowflakeFile.from_path('@image_stage', file_path)' correctly creates Snowflake file object based on provided file path - The UDF registration specifies the correct input type 'StringType' for file path , BinaryType' for the return and includes the 'pillow' package. - The final select statement calls the UDF correctly with the 'relative_path' column. Option D is incorrect because 'f.readall()' returns bytes, and PIL.lmage.open expects a file-like object or filename, wrapping this within ByteslO is unnecessary. Option B is wrong because the snowflake file object should be created in the python function itself, UDF creation and calling both are wrong Option A and C have registration issue for packages, as it is not provided to function registration.

NEW QUESTION # 224
You have a Snowpark Python application that performs several data transformations on a DataFrame representing customer transactions. The application is experiencing performance issues, and you suspect that some transformations are unnecessarily expensive. Which of the following techniques can MOST effectively optimize the performance of your Snowpark application, specifically focusing on minimizing data movement and leveraging Snowflake's query optimization capabilities?

A. Leverage Snowpark's built-in DataFrame transformations (e.g., .groupBy()') to allow Snowflake to optimize the query execution plan. Avoid pulling large amounts of data into the client application for simple operations. Only call 'collect()' as the last and final option, as this is the most costly activity of all.
B. Take the dataframe to Pandas dataframe as soon as possible in between transformations, since Pandas dataframes will be faster.
C. Explicitly call .cache()' on the DataFrame after each transformation to materialize intermediate results in memory.
D. Always use the largest available Snowflake warehouse size to ensure sufficient compute resources.
E. Use User-Defined Functions (UDFs) written in Python for all transformations, regardless of their complexity.

Answer: A

Explanation:
Snowpark is designed to push down computations to Snowflake, allowing Snowflake's query optimizer to handle the execution. Using Snowpark's built-in DataFrame transformations allows Snowflake to understand the intent and optimize the query accordingly. Materializing intermediate results using .cache()' (A) can lead to unnecessary data movement. Python UDFs (B) can be useful for complex logic but should be avoided for simple transformations as they bypass Snowflake's optimization capabilities and are generally slower than native SQL functions. Warehouse size (E) is a factor, but optimizing the query logic is more crucial. Using Pandas dataframe is also costly and performance heavy.

NEW QUESTION # 225
A data engineering team has created several Snowpark Python UDFs and UDTFs in the 'TRANSFORMATIONS' schema of the 'ANALYTICS' database. A data science team needs to use these functions in their data analysis notebooks. What is the MINIMUM set of privileges that must be granted to the data science team's role ('DATA SCIENTIST') to allow them to discover and execute these UDFs and UDTFs?

A. GRANT USAGE ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT USAGE ON SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST; GRANT ALL PRIVILEGES ON ALL FUNCTIONS IN SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;
B. GRANT USAGE ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT USAGE ON SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;
C. GRANT EXECUTE ON ALL FUNCTIONS IN SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;
D. GRANT USAGE ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT USAGE ON SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST; GRANT EXECUTE ON ALL FUNCTIONS IN SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST,
E. GRANT ALL PRIVILEGES ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT ALL PRIVILEGES ON SCHEMA ANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;

Answer: D

Explanation:
The 'USAGE privilege on the database and schema is required for the role to discover (see) the UDFs and UDTFs. The 'EXECUTE privilege on the functions themselves is required to execute them. 'ALL PRIVILEGES' is an overly permissive grant and not the minimum required. Option D is missing the execute privilege. Option E is missing USAGE on Database and Schema.

NEW QUESTION # 226
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