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| Section | Weight | Objectives |
|---|---|---|
| Data Transformations and DataFrame Operations | 35% | - Persisting transformed data - Filtering, Aggregating, and Joining DataFrames - Using built-in functions - Complex data pipelines - Window functions |
| Snowpark Concepts | 15% | - Client-side vs. Server-side execution - Snowpark DataFrames and query plans - Snowpark architecture and core concepts - Transformations vs. Actions - Snowpark Sessions and connection management - Stored procedures and conditional logic |
| Snowpark API for Python | 30% | - Working with Semi-structured data - Reading and writing data - DataFrame creation and manipulation - Establishing connections and session management - User-Defined Functions (UDFs) and Stored Procedures |
| Performance Optimization and Best Practices | 20% | - Vectorized UDFs - Debugging and explain plans - Caching strategies - Minimizing data transfer - Warehouse sizing for Snowpark - Query pushdown and optimization |
1. 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?
A)
B)
C)
D)
E) 
2. 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?
A)
B)
C)
D)
E) 
3. You have a Snowpark Python application that performs complex data transformations and machine learning model training. The data is stored in Snowflake tables. You notice that model training jobs, specifically those involving large feature sets and iterative algorithms, are consistently slow. The warehouse is already scaled to a LARGE size. Which of the following techniques, when applied individually or in combination, would MOST likely improve the performance of model training in Snowpark?
A) Cache intermediate Snowpark DataFrames using to avoid recomputation of common data transformations across multiple training iterations.
B) Scale the virtual warehouse UP to an XLARGE or larger. This provides more computational resources.
C) Implement data skipping and filtering strategies to reduce the amount of data read during feature extraction and model training. Pre-aggregate when possible.
D) Use the 'sproc' decorator to define user-defined functions (UDFs) directly within Snowflake, leveraging the platform's optimized execution engine for specific computations.
E) Leverage external functions (IJDFs) to offload computationally intensive operations to specialized hardware outside of Snowflake.
4. You have a Snowflake table 'raw_events' containing JSON data in a VARIANT column named 'event_data'. This column contains nested JSON objects representing user activity on a website. You need to extract specific nested values and load them into a new Snowflake table 'user_activity' with columns 'event_type' , and 'timestamp'. Which of the following Snowpark code snippets is the MOST efficient and correct way to achieve this, assuming you want to minimize data transfer and optimize performance? Consider the potential for null values within the JSON.
A)
B)
C)
D)
E) 
5. You have a Snowpark DataFrame named 'orders_df with columns 'order_id', 'customer_id', 'order_date', and 'order_total'. You need to perform the following data enrichment steps using Snowpark for Python: 1. Calculate the 'year' from the 'order_date' column. 2. Calculate the 'discounted_total' by applying a discount of 10% if the 'order_total' is greater than $100, otherwise, no discount. 3. Create a new column 'customer_tier' based on the total spend per customer for each year. Customers with total spend greater than $1000 are 'Gold', between $500 and $1000 are 'Silver', and below $500 are 'Bronze'. Which of the following code snippets correctly implements these data enrichment steps using Snowpark (Assume the existence of a customer total spend df DataFrame).
A)
B)
C)
D)
E) 
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: A | Question # 3 Answer: A,C | Question # 4 Answer: C | Question # 5 Answer: D |
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