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| Section | Weight | Objectives |
|---|---|---|
| Snowpark API for Python | 30% | - Working with Semi-structured data - DataFrame creation and manipulation - User-Defined Functions (UDFs) and Stored Procedures - Establishing connections and session management - Reading and writing data |
| Snowpark Concepts | 15% | - Transformations vs. Actions - Stored procedures and conditional logic - Snowpark architecture and core concepts - Snowpark Sessions and connection management - Snowpark DataFrames and query plans - Client-side vs. Server-side execution |
| Performance Optimization and Best Practices | 20% | - Debugging and explain plans - Minimizing data transfer - Warehouse sizing for Snowpark - Caching strategies - Query pushdown and optimization - Vectorized UDFs |
| Data Transformations and DataFrame Operations | 35% | - Persisting transformed data - Window functions - Complex data pipelines - Filtering, Aggregating, and Joining DataFrames - Using built-in functions |
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NEW QUESTION # 280
You have a Snowpark Python application that reads data from a Snowflake table named 'SALES DATA', performs several transformations using DataFrames, and then writes the results back to a new table named 'AGGREGATED SALES'. The application runs successfully, but you notice that the write operation to 'AGGREGATED SALES' is consistently slow. After examining the query profile, you observe significant skew in the data being written, causing some worker nodes to be overloaded. Which of the following techniques could you use within your Snowpark application to mitigate the data skew and improve the write performance to 'AGGREGATED SALES'?
Answer: A,E
Explanation:
Both options B and D address data skew directly. Option B, , attempts to redistribute data evenly, which can alleviate skew if the repartitioning strategy is effective (e.g., using a hash function). Option D, using a UDF and 'repartitionByRange' , allows for more sophisticated custom partitioning based on the skew key, potentially achieving a more balanced distribution. Increasing warehouse size (A) might provide more resources, but it doesn't directly address the skew. Sorting (C) can exacerbate skew by concentrating similar values on single nodes. Clustering (E) improves read performance after the data is written, but does not improve the write performance itself. Therefore, B and D are the best choices to reduce skew during the write operation.
NEW QUESTION # 281
You have a Snowpark Python stored procedure 'process_data' that takes a Snowpark DataFrame as input, performs several data transformations using functions defined in a separate Python module 'data utils.py', and returns a transformed DataFrame. The 'data utils.py' file is located in your local directory. You want to register this stored procedure so that it can be called from Snowflake. Which of the following code snippets demonstrate(s) the correct way to register the stored procedure, ensuring that the 'data utils.py' module is available within the Snowpark environment? (Select TWO)
Answer: A,E
Explanation:
Option C correctly specifies the 'data_utils.py' file in the 'imports' parameter of the gsproc' decorator, making it available within the Snowpark environment. It also includes re-importing the 'data_utilS package within the function. Option D specifies the file in the 'imports parameter and uses the '_import_' function to load and use the module, which is a valid approach. Option A would error at runtime because "data_utils' won't be found unless it is in the same file as the stored procedure definition or the user has installed the python packages. Option B contains incorrect usage of 'return_type', and and doesn't account for importing 'data_utils.pV. Option E has the same problem as A as the dependent file 'data_utils.py' has not been imported. The 'stage_location' parameter specifies the stage where the Python file should be uploaded. The two correct ways ensure that the data_utils module will be loaded and accessible in the Snowpark environment.
NEW QUESTION # 282
A Snowpark application is designed to process data residing in a Snowflake table called 'ORDERS'. The application needs to create a temporary view named 'TEMP ORDERS VIEW based on a filtered subset of this table. The view should only be accessible within the current Snowpark session and should be automatically dropped when the session ends. What is the MOST efficient and correct Python code snippet using Snowpark to achieve this?
Answer: D
Explanation:
Option A, , is the most efficient and directly creates a temporary view scoped to the session. The 'createOrRepIaceTempView' method is the correct Snowpark API call for creating such views. Options B and D, 'createOrReplaceView' with or 'createView' with , are not valid Snowpark API calls. Option C, createOrReplaceGlobalTempViews , creates a global temporary view, accessible across sessions, which is not the requirement. Option E is adding a redundant step by creating a dataframe from another dataframe.
NEW QUESTION # 283
You are working with semi-structured data in Snowflake stored in a VARIANT column named 'payload'. You want to extract specific fields from this VARIANT column within a SQL query used to create a Snowpark DataFrame. Which of the following approaches allows you to access nested fields within the 'payload' column directly in the SQL query and create a corresponding column in your Snowpark DataFrame? Select all that apply.
Answer: B,C,D
Explanation:
Options A, B, and D are correct. Option A utilizes the Snowflake's native dot notation (e.g., 'payload:fieldl :field2) for direct access of nested fields. Option B provides the 'GET_PATH' function, also allowing access to nested fields. Option D leverages 'LATERAL FLATTEN' to unnest the VARIANT data, enabling subsequent field access. Option C is less efficient, adding unnecessary steps, and Option E involves Pandas, which is typically not the optimal path for leveraging Snowpark's capabilities directly. Remember that 'LATERAL FLATTEN' is best when you need to process the data in a relational format after extracting it from the VARIANT.
NEW QUESTION # 284
You are developing a data pipeline using Snowpark and want to optimize the execution of multiple DataFrame transformations. Which of the following strategies or techniques can you employ to improve performance and reduce execution time? (Select all that apply)
Answer: B,E
Explanation:
Options C and E are correct. Option C, pushdown optimization by ensuring filter operations are applied as early as possible, is a key optimization technique. UDFs written in Scala can also be optimized by the compiler and Snowflake's engine. Option E, using , is the correct way to cache intermediate DataFrames for reuse, preventing redundant computations. Option A is incorrect; eagerly evaluating DataFrames with 'collect()' defeats the purpose of lazy evaluation and can significantly degrade performance. Option B is not directly applicable to Snowpark DataFrame transformations; 'CACHE RESULT is primarily for SQL queries executed outside of Snowpark DataFrame operations. Option D, is not a valid function in Snowpark API.
NEW QUESTION # 285
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