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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowpark Concepts | 15% | - Snowpark architecture and core concepts - Stored procedures and conditional logic - Snowpark Sessions and connection management - Transformations vs. Actions - Client-side vs. Server-side execution - Snowpark DataFrames and query plans |
| Topic 2: Performance Optimization and Best Practices | 20% | - Debugging and explain plans - Query pushdown and optimization - Minimizing data transfer - Vectorized UDFs - Caching strategies - Warehouse sizing for Snowpark |
| Topic 3: Data Transformations and DataFrame Operations | 35% | - Complex data pipelines - Persisting transformed data - Using built-in functions - Filtering, Aggregating, and Joining DataFrames - Window functions |
| Topic 4: Snowpark API for Python | 30% | - DataFrame creation and manipulation - Working with Semi-structured data - User-Defined Functions (UDFs) and Stored Procedures - Establishing connections and session management - Reading and writing data |
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NEW QUESTION # 358
You are working with Snowpark and a DataFrame named 'orders df that contains order data, including a column named 'items' which is a VARIANT type and holds an array of JSON objects, where each object represents an item in the order. You need to explode this array into separate rows, extracting the 'item_id' and 'quantity' for each item. Which of the following Snowpark snippets correctly performs this transformation AND handles potential NULL or empty arrays in the 'items' column?
Answer: C
Explanation:
Option E is the most robust. It explicitly handles NULL or empty arrays in the 'items' column by replacing them with an empty array before exploding. This prevents errors during the explode operation. The other options will fail if the array is NULL because you cannot explode a NULL array. Additionally the items column need to be dropped after explode, so it won't be there in the last query. Column 'col' created by expload function must be used to extract the 'item_id' and 'quantity' values.
NEW QUESTION # 359
You are developing a Snowpark application to perform complex data transformations on a large dataset stored in Snowflake. You need to optimize the application's performance. Which of the following strategies are MOST effective for improving performance within Snowpark?
Answer: D,E
Explanation:
Snowpark's optimized functions (B) are designed to be executed efficiently within the Snowflake engine. Lazy evaluation (E) allows Snowpark to optimize the entire query plan before execution. UDFs (A) can be useful, but should be used judiciously as they can introduce overhead. Materializing intermediate DataFrames (C) can increase storage costs and may not always improve performance. 'collect()' (D) should be avoided for large datasets as it moves data out of Snowflake.
NEW QUESTION # 360
You are working with a Snowpark DataFrame 'transactions df that contains customer transaction data'. This data includes a 'transaction amount' column and a 'transaction date' column. You need to create a new feature called 'is weekend transaction' that indicates whether a transaction occurred on a weekend (Saturday or Sunday). Furthermore, some 'transaction_date' values are missing. You want to impute the missing dates with the mode (most frequent date) before determining if the transaction occurred on a weekend. Which of the following steps, when combined, provide the correct and most efficient approach to achieve this?
Answer: A
Explanation:
Option B is the most efficient and utilizes Snowpark's built-in capabilities. It calculates the mode using Snowpark's aggregation functions, fills missing values using and leverages the function to determine weekend status without the need for a UDF. Option A creates a UDF which is less efficient than using a built-in function. Option C replaces with an arbitary string which is bad as its hardcoding and not efficient, after filling the value a UDF is made which is not efficient as well, Also after that the data has to converted back, thus option C is not correct. Options D is more complex as it utilizes temporary table which is not efficient. Option E create a UDF when snowpark provides readily available functions. so its less efficient.
NEW QUESTION # 361
A data engineer is tasked with calculating a 3-month rolling average of sales data using Snowpark Python. The sales data is stored in a table named 'SALES DATA' with columns 'sale_date' (DATE) and (NUMBER). They need to use a table function to accomplish this efficiently. Which of the following Snowpark Python code snippets correctly implements this rolling average calculation using a table function?
Answer: C
Explanation:
Option E correctly defines a table function that takes a DataFrame as input and returns a DataFrame with the rolling average calculated using the 'avg()' function and a window specification. It then uses 'session.table_function' to apply the table function to the DataFrame and selects the required columns. Option A attempts to call directly within the table function definition which is invalid. Option B passes 'session' as the first argument to table function that's wrong. Option C defines that aren't compatible with DataFrame operations, this approach is not how you should do. Option D is same as C, and incorrect because, it attempts to use window functions inside the table function where the input is a table function input types.
NEW QUESTION # 362
You are developing a Snowpark application that requires calling a stored procedure. Which of the following approaches is the MOST secure and efficient way to call a stored procedure from your Snowpark Python code, assuming the stored procedure returns a single value?
Answer: B
Explanation:
The 'session.call()' method (B) is the most direct and efficient way to call a stored procedure and retrieve its single return value from Snowpark Python. It handles the execution and data retrieval efficiently. Using 'session.sql()' (A and C) is less efficient as it requires parsing SQL and manual extraction of the result. 'session.sproc()' (D) is generally used to register Python functions as stored procedures and not for calling existing ones. is not valid, 'session.call()' is the right way to call procedure
NEW QUESTION # 363
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