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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Transformations and DataFrame Operations | 35% | - Complex data pipelines - Filtering, Aggregating, and Joining DataFrames - Window functions - Persisting transformed data - Using built-in functions |
| Topic 2: Performance Optimization and Best Practices | 20% | - Vectorized UDFs - Debugging and explain plans - Query pushdown and optimization - Minimizing data transfer - Caching strategies - Warehouse sizing for Snowpark |
| Topic 3: Snowpark Concepts | 15% | - Snowpark DataFrames and query plans - Transformations vs. Actions - Client-side vs. Server-side execution - Snowpark architecture and core concepts - Snowpark Sessions and connection management - Stored procedures and conditional logic |
| Topic 4: Snowpark API for Python | 30% | - User-Defined Functions (UDFs) and Stored Procedures - DataFrame creation and manipulation - Working with Semi-structured data - Reading and writing data - Establishing connections and session management |
You are tasked with creating a Snowpark stored procedure that needs to access a secret stored in Snowflake's Secret Managen The secret contains credentials required to connect to an external API. Which of the following steps are necessary to correctly and securely access and use the secret within your Snowpark stored procedure? (Select all that apply)
Correct Answer: B,D,E 🗳️
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You have a Snowpark DataFrame 'product_df and you want to create a view named 'active_products' that only includes products with a 'status' column value equal to 'ACTIVE'. This view should be accessible to all users in the 'PUBLIC' role. Which of the following code snippets correctly creates the view and grants the necessary privileges?





Correct Answer: E 🗳️
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You have a Snowpark DataFrame with columns 'sale_date', 'product_id', and 'revenue'. You need to calculate the cumulative revenue for each product over time. Which of the following approaches will accomplish this in Snowpark using window functions?





Correct Answer: A,D 🗳️
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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?
Correct Answer: E 🗳️
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You have a Snowflake table named 'RAW EVENTS with a large number of events data, containing columns like 'EVENT ID', 'TIMESTAMP, 'USER ID, and 'EVENT_TYPE. The 'EVENT TYPE column contains string values representing different event categories. You want to create a Snowpark DataFrame, but due to the table's size, you only want to sample a small portion of the data for initial exploration and testing. Which of the following code snippets MOST accurately and efficiently creates a sampled Snowpark DataFrame named 'sampled_df containing approximately 1% of the rows from the 'RAW EVENTS table?





Correct Answer: A 🗳️
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