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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowpark Concepts | 15% | - Transformations vs. Actions - Client-side vs. Server-side execution - Snowpark DataFrames and query plans - Stored procedures and conditional logic - Snowpark architecture and core concepts - Snowpark Sessions and connection management |
| Topic 2: Data Transformations and DataFrame Operations | 35% | - Complex data pipelines - Window functions - Persisting transformed data - Using built-in functions - Filtering, Aggregating, and Joining DataFrames |
| Topic 3: Snowpark API for Python | 30% | - User-Defined Functions (UDFs) and Stored Procedures - Working with Semi-structured data - Reading and writing data - DataFrame creation and manipulation - Establishing connections and session management |
| Topic 4: Performance Optimization and Best Practices | 20% | - Minimizing data transfer - Caching strategies - Debugging and explain plans - Warehouse sizing for Snowpark - Vectorized UDFs - Query pushdown and optimization |
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NEW QUESTION # 146
You have a Snowflake table 'PRODUCT CATALOG' with columns 'PRODUCT ID, 'PRODUCT NAME, and 'CATEGORY ID. You also have a table 'CATEGORY' with 'CATEGORY ID' and 'CATEGORY NAME. You need to create a Snowpark DataFrame that joins these two tables and includes only 'PRODUCT NAME and 'CATEGORY NAME. Assume a Snowpark Session object named 'session' is available. Which code snippet demonstrates creating the DataFrame using Snowpark's join functionality and column selection while using the 'table' method?
Answer: A
Explanation:
Option D correctly joins the tables using the 'join' method with a column expression specifying the join condition. It also correctly selects only the desired columns using and option A incorrectly uses 'PRODUCT_ID instead of for the join and needs 'col()' to reference columns. option B selects 'PRODUCT_NAME', 'CATEGORY_NAME' without using 'col(Y which will cause an error since the join brings duplicate column names from product_df and category_df. Option C uses Pythonic '[['PRODUCT_NAME', 'CATEGORY_NAME'I]' which only works on Pandas dataframes. option E attempts to use col() to reference the columns in the join condition which is incorrect as it is a column expression needs to use fully qualified table name for the same named column in two dataframes; moreover selection is missing col().
NEW QUESTION # 147
You are working with a Snowpark DataFrame named 'sales df' containing sales data including columns 'product id', 'sale date', and 'sale_amount'. You want to create a new DataFrame 'filtered df that only includes rows where the 'product id' is present in a list of approved product IDs and the 'sale_amount' is greater than the average sale amount. You have already calculated the average sale amount and stored it in a variable named 'avg_sale amount'. Which of the following code snippets correctly achieves this?
Answer: A,C,D
Explanation:
Options B, C, and E are correct. Option B uses chained .filter() methods which is valid. Option C uses chained .where() methods which is functionally equivalent to filter. Option E correctly applies both conditions within the filter. Option A is incorrect because the '&' operator has different precedence compared to the bitwise & operator and can cause issues with Snowpark type checking; also, it does not have parentheses for the individual condition. Option D is incorrect because .contains() is typically for checking if a string contains a substring or if an array contains a specific value, not for checking if a value is in a list.
NEW QUESTION # 148
You have a Snowpark Python application that reads data from multiple Snowflake tables, performs complex transformations using UDFs, and writes the results to a new table. During peak hours, the application experiences performance bottlenecks. The Snowflake warehouse associated with the Snowpark session is already configured with the 'SNOWPARK OPTIMIZED warehouse type. Which of the following strategies, when implemented together, would BEST improve the application's performance?
Answer: B,C
Explanation:
Increasing the warehouse size provides more compute resources. Partitioning tables improves join performance. Optimizing UDFs reduces execution time. Utilizing vectorized UDFs allows for processing batches of data at once, reducing overhead. Snowpark's optimized join operations use efficient algorithms. Options A and C, while helpful, don't address the underlying issues as directly. Caching might help repetitive tasks, rewriting UDFs in SQL isn't always feasible or optimal if specialized logic is implemented. Option E is most optimal because it also utilizes vectorized UDFs where possible.
NEW QUESTION # 149
You have written a Snowpark Python function that utilizes a UDF to perform complex string manipulation on a DataFrame containing customer reviews. When deploying this function using '@sproc.test_utils.mock_snowflake environment, the test fails with a 'ModuleNotFoundError' indicating that a custom Python library (e.g., is not available. You have already confirmed that the library is installed in your local development environment. What is the MOST reliable way to ensure the UDF has access to this dependency during local testing?
Answer: A
Explanation:
Option C is the most reliable solution for local testing with . The function explicitly makes the library available to the Snowpark session during execution, simulating how dependencies are handled in the Snowflake environment. Option A might work locally, but it's not a reliable deployment strategy. Option B is a temporary workaround and not a structured solution. Option D makes the library globally available, defeating the purpose of isolating dependencies for testing. Option E is a valid approach but less explicit and may affect other Python environments on the system. Using 'add_import' ensures that the correct version and dependencies are included in the deployment package.
NEW QUESTION # 150
Which of the following statements are correct regarding account identifiers and their usage when creating Snowpark sessions in Python?
Answer: B,D
Explanation:
Account identifiers provide a unique way to connect to Snowflake accounts. If the identifier includes the region ID, specifying it separately is redundant. Snowflake supports both 'Organization Name-Account Name' and legacy account locators. Option A is incorrect because account identifiers are case-insensitive. Option B is incorrect, account identifiers can be used across regions. Option D is incorrect.
NEW QUESTION # 151
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