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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowpark API for Python | 30% | - Working with Semi-structured data - DataFrame creation and manipulation - User-Defined Functions (UDFs) and Stored Procedures - Reading and writing data - Establishing connections and session management |
| Topic 2: Data Transformations and DataFrame Operations | 35% | - Persisting transformed data - Filtering, Aggregating, and Joining DataFrames - Window functions - Complex data pipelines - Using built-in functions |
| Topic 3: Snowpark Concepts | 15% | - Transformations vs. Actions - Snowpark Sessions and connection management - Stored procedures and conditional logic - Snowpark DataFrames and query plans - Client-side vs. Server-side execution - Snowpark architecture and core concepts |
| Topic 4: Performance Optimization and Best Practices | 20% | - Minimizing data transfer - Debugging and explain plans - Query pushdown and optimization - Caching strategies - Warehouse sizing for Snowpark - Vectorized UDFs |
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NEW QUESTION # 130
You are migrating a Pandas-based data processing pipeline to Snowpark to leverage Snowflake's scalability and performance. One part of the pipeline involves a computationally intensive custom function that is applied row-by-row to a DataFrame using the 'apply' method in Pandas. When migrating this to Snowpark, what are the most effective strategies for achieving similar functionality while maximizing performance within the Snowflake environment?
Answer: B,E
Explanation:
Vectorized operations in Snowpark provide the best performance by leveraging Snowflake's distributed processing. Creating a UDF allows you to push the computation to the Snowflake engine, avoiding the need to transfer large amounts of data to the Python environment. Direct translation to Snowpark 'apply' is not available as Snowpark 'apply' is significantly different, pandas code requires explicit data copying from and to snowflake. Stored procedures do not leverage the parallel processing capabilities of Snowflake as effectively as UDFs or vectorized operations. Pandas API is not the recommended way as UDF or vectorized operation.
NEW QUESTION # 131
Consider a Snowflake table 'sales_data' with a VARIANT column 'order_details' containing an array of JSON objects, where each object represents an item in an order. Each item object has fields like 'quantity', and 'price'. You need to calculate the total price for each order by summing the product of 'quantity' and 'price' for all items in the 'order_details' array. Which of the following Snowpark Python snippets correctly accomplishes this?
Answer: C
Explanation:
Option D first flattens the array of order items using 'flatten' , creating a new row for each item. It then calculates the product of 'quantity' and 'price' for each item and sums these products to get the total price. Option A and C will not work, because they doesn't flatten the array first. Option E should use flatten instead of explode.
NEW QUESTION # 132
You are tasked with deploying a Snowpark Python application that utilizes a third-party library, 'scikit-learn' , for machine learning tasks. The application will be executed as a Snowflake Stored Procedure. What are the necessary steps to ensure the 'scikit-learn' library is available within the Snowpark environment?
Answer: D
Explanation:
The correct approach is to create a Snowflake Anaconda environment with the required packages and then specify that environment when creating the Snowpark Stored Procedure. This ensures that the environment is available during execution. A is incorrect as session.add_import() is for local file imports. B is not the recommended and reliable approach. C is overly complex. E is incorrect as the environment in Snowflake must contain the dependency.
NEW QUESTION # 133
You have two Snowpark DataFrames, 'dfl' and 'df2', representing customer data'. 'dfl' contains customer IDs and names, while 'df2' contains customer IDs and email addresses. You need to create a new DataFrame that contains all customer IDs, names, and email addresses, including customers present in only one of the DataFrames. Which Snowpark set operation and join type would be most appropriate for achieving this?
Answer: B
Explanation:
Option C is the correct answer. 'UNION' is used to combine the rows from both DataFrames, removing duplicate rows. T-ULL OUTER JOIN' is used to include all rows from both DataFrames, even if there is no matching customer ID in the other DataFrame. The combination of 'UNION' and FULL OUTER JOIN' ensures that all customers and their associated information are included in the resulting DataFrame. The other options would either result in only matching records, missing records, or incorrect combination.
NEW QUESTION # 134
You have two Snowpark DataFrames, 'customers' and 'orders'. The 'customers' DataFrame has columns and 'customer name'. The 'orders' DataFrame has columns 'order id', 'customer id', and 'order amount'. You need to find all customers who have NOT placed any orders. Which of the following Snowpark set operations correctly implements this?
Answer: E
Explanation:
Option D is correct. It first selects the distinct 'customer_id' from both DataFrames. Then, it uses the 'minus' (or 'except_') set operation to find the difference between the customer IDs in the 'customers DataFrame and the customer IDs in the 'orders' DataFrame. This effectively returns the customer IDs of customers who have not placed any orders. The 'join' operation is not a set operation, and options B and C are not valid syntax for Snowpark. Option E is syntactically correct in Snowpark, and equivalent to Option D. However, since the question has to have one answer, Option D is kept.
NEW QUESTION # 135
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