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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Performance Optimization and Best Practices | 20% | - Debugging and explain plans - Query pushdown and optimization - Minimizing data transfer - Warehouse sizing for Snowpark - Vectorized UDFs - Caching strategies |
| Topic 2: Data Transformations and DataFrame Operations | 35% | - Using built-in functions - Complex data pipelines - Window functions - Filtering, Aggregating, and Joining DataFrames - Persisting transformed data |
| Topic 3: Snowpark API for Python | 30% | - User-Defined Functions (UDFs) and Stored Procedures - Reading and writing data - Working with Semi-structured data - DataFrame creation and manipulation - Establishing connections and session management |
| Topic 4: Snowpark Concepts | 15% | - Snowpark architecture and core concepts - Snowpark DataFrames and query plans - Stored procedures and conditional logic - Snowpark Sessions and connection management - Transformations vs. Actions - Client-side vs. Server-side execution |
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NEW QUESTION # 283
Consider the following Snowpark Python code snippet designed to read data from a Snowflake table, apply a user-defined function (UDF) for data transformation, and then write the transformed data to another table. The UDF, 'calculate_score' , requires a configuration file ('config.json') to be loaded. Which of the following code snippets demonstrates the CORRECT and MOST efficient way to load and access the "config.json' file within the UDF, ensuring that it's available to all UDF invocations without requiring network access?
Answer: A
Explanation:
Option E is the most correct answer, leveraging the 'imports' parameter to efficiently load the 'config.json' file from the specified stage into the UDF's environment. The file is then accessed using a relative path ('config.json'), which is the location where Snowflake places the imported file. Option A is incorrect because it attempts to load from '/tmp/config.json' , which is not accessible within the UDF's environment. Option B is incorrect due to it being Permanent UDF and trying to access the session which are mutually exclusive. In addition , the stage location must be at the time of registration rather than in the UDF definitition, which isn't right. Option C is almost right but incorrect because it accesses file with 'os.path.join(os.getcwd(), 'config.json'Y which is unecessary and wrong. Option D is incorrect because files specified in the 'imports' parameter are available in the current working directory directly, no need to import and declare a global variable
NEW QUESTION # 284
You have a Snowpark Python stored procedure named 'calculate_stats' that takes a table name as input and returns summary statistics. You need to modify the stored procedure to add a new optional parameter for specifying a filter condition. Which of the following SQL commands, used in conjunction with the Snowpark API for Python, is the MOST efficient way to alter the existing stored procedure without dropping and recreating it?
Answer: B
Explanation:
'CREATE OR REPLACE PROCEDURE is the standard and efficient way to modify stored procedures in Snowflake, including adding optional parameters. It avoids dropping and recreating the procedure, preserving grants and dependencies. Option B accurately defines a new procedure definition that incorporates both input parameters and sets a default NULL value for the filter condition which makes it optional. Other Options not efficient , Option A, drops old and creates new SP. Options C & D are invalid SQL Syntax, and E makes the FILTER_CONDITION mandatory.
NEW QUESTION # 285
You are tasked with optimizing a Snowpark Python application that performs complex geospatial calculations on a large dataset. The application experiences significant performance bottlenecks due to the computational intensity of the geospatial functions. Which of the following strategies would be MOST effective in improving performance?
Answer: A
Explanation:
Vectorized UDFs written in Java or Scala offer significant performance gains compared to Python UDFs due to their lower overhead and ability to leverage JVM optimizations. Increasing the warehouse size (A) might help, but it's not the most targeted solution for computationally intensive tasks. Partitioning (C) can help with data distribution, but the bottleneck remains the calculation itself. Native Python libraries (D) might not be as performant as optimized JVM-based UDFs. Disabling query optimization (E) is generally not recommended and can negatively impact performance.
NEW QUESTION # 286
Consider the following Snowpark Python code snippet intended to operationalize a stored procedure:
This code results in an error during registration. Which of the following actions would be MOST appropriate to fix the registration issue and ensure the stored procedure can be successfully operationalized?
Answer: B
Explanation:
Option C is the correct solution. The 'session.sproc.registefl method should be called outside of the 'main' function definition. The registration process is a separate step from the function definition itself. The 'sproc.register' will take the function definition and create an object in Snowflake. Since this must be outside of the function it needs to be outside the main function. All other options would not solve the error with the registration.
NEW QUESTION # 287
You have created a Python UDTF in Snowpark to process large volumes of image data'. This UDTF resizes each image and extracts certain features from it. The process is memory-intensive and sometimes fails due to Python process exceeding memory limits. You need to optimize this UDTF for memory usage. Which of the following strategies would be MOST effective? (Select TWO)
Answer: A,B
Explanation:
Lazy evaluation (B) and iterators allow processing data in smaller chunks, reducing memory consumption. Utilizing image compression techniques (D) reduces the memory footprint of each image, allowing more images to be processed within the available memory. Using scalar UDFs would not help and might perform worse. Decreasing the warehouse size would not solve the underlying problem and may make it worse. MAX_BATCH_SIZE determines how many records are sent over to UDF for each batch. It can help increasing performance but decreasing batch sizes might help with memory management, however its dependent on each case.
NEW QUESTION # 288
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