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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowpark Concepts | 15% | - Snowpark architecture and core concepts - Snowpark Sessions and connection management - Stored procedures and conditional logic - Transformations vs. Actions - Snowpark DataFrames and query plans - Client-side vs. Server-side execution |
| Topic 2: Performance Optimization and Best Practices | 20% | - Query pushdown and optimization - Vectorized UDFs - Caching strategies - Warehouse sizing for Snowpark - Debugging and explain plans - Minimizing data transfer |
| Topic 3: Data Transformations and DataFrame Operations | 35% | - Persisting transformed data - Complex data pipelines - Filtering, Aggregating, and Joining DataFrames - Window functions - Using built-in functions |
| Topic 4: Snowpark API for Python | 30% | - Establishing connections and session management - Reading and writing data - DataFrame creation and manipulation - User-Defined Functions (UDFs) and Stored Procedures - Working with Semi-structured data |
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NEW QUESTION # 116
You have a Snowpark DataFrame with columns 'order_id', 'product_id', 'sale_date' (DATE), and 'sale_amount'. You need to perform the following transformations: 1. Filter out sales records before January 1, 2023.2. Group the data by 'product_id' and calculate the total 'sale_amount' for each product. 3. Create a new column 'average_sale_amount' by dividing the total 'sale_amount' by the number of distinct 'order_id' for each product. You must alias the aggregate function. Which of the following Snowpark code snippets correctly implements these transformations?
Answer: D
Explanation:
Option E is the most appropriate and correct. It uses for explicit date conversion, ensures correct filtering, aggregates with aliases, and calculates the 'average_sale_amount' correctly using the aliased columns. Options A, B, C and D are all valid for Snowflake. Option B incorrectly passes the date string. 'as_' instead of 'alias' are not valid and will cause an issue.
NEW QUESTION # 117
You are developing a Snowpark stored procedure in Python that needs to access and modify a temporary table within the same session.
Which of the following approaches is the MOST efficient and recommended way to achieve this?
Answer: A
Explanation:
Option B, using 'session.createDataFrame()' and DataFrame transformations, is the most efficient and recommended approach. Snowpark DataFrames are optimized for execution within the Snowflake engine. Using DataFrame transformations allows Snowpark to leverage its query optimization capabilities. Option A, using 'session.sql()' repeatedly, involves parsing and executing SQL statements for each operation, which is less efficient. Option C, using a separate connection, introduces unnecessary overhead and complexity. Option D, global temporary tables, are not session-specific. Option E, creating a temporary view and then querying it with SQL, is also less efficient than using DataFrame operations directly.
NEW QUESTION # 118
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: E
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 # 119
A data science team wants to operationalize a Snowpark Python UDF that performs sentiment analysis on customer reviews. The UDF, 'analyze sentiment(review_text)', is currently defined within a Snowpark session. Which of the following approaches is the MOST efficient and scalable way to deploy this UDF for real-time scoring of incoming review data in a Snowflake table named 'CUSTOMER REVIEWS'?
Answer: D
Explanation:
Registering the UDF as a persistent UDF in Snowflake allows it to be called directly from SQL, leveraging Snowflake's query engine for optimal performance and scalability. Option A keeps the transformation entirely within Snowpark, which is a valid approach, but less scalable. Option C involves converting to a Pandas DataFrame, which moves data outside of Snowflake and negates its benefits. Option D creates a stored procedure that is correct but the question ask to perform in real time so it's not ideal. Option E is not the way to persist Snowpark with UDF.
NEW QUESTION # 120
You are using Snowpark in Python within a Jupyter Notebook environment to analyze sales data'. You've established a connection to Snowflake and loaded your data into a Snowpark DataFrame named 'sales ff. You need to calculate the cumulative sales for each product category over time. The 'sales_df' DataFrame has columns 'SALE DATE' (DATE), 'PRODUCT CATEGORY' (VARCHAR), and 'SALE AMOUNT' (NUMBER). Which of the following approaches, or combination of approaches, will correctly calculate the cumulative sales while optimizing for Snowflake's performance and scalability? (Select all that apply)
Answer: B,D,E
Explanation:
Options A, C, and E are correct. Option A utilizes Snowpark's DataFrame transformations with window functions, which are optimized for Snowflake's engine and can efficiently handle large datasets. This leverages Snowflake's parallel processing capabilities. Option C involves creating a stored procedure within Snowflake. This approach pushes the computation to the Snowflake server, potentially improving performance, especially if the cumulative sales calculation is complex. This also benefits from Snowflake's optimization capabilities. Option E executes a SQL query with a window function that's optimized. Option B is incorrect because iterating through rows in a Jupyter Notebook will be extremely slow and inefficient for large datasets. It defeats the purpose of using Snowpark for distributed data processing. Calculating cumulative sales this way doesn't leverage Snowflake's capabilities. Option D is partially correct in that it orders the data, but using a UDF for cumulative sum calculation will likely be less efficient than using native window functions within Snowflake.
NEW QUESTION # 121
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