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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Performance Optimization and Best Practices | 20% | - Minimizing data transfer - Warehouse sizing for Snowpark - Query pushdown and optimization - Debugging and explain plans - Caching strategies - Vectorized UDFs |
| Topic 2: Data Transformations and DataFrame Operations | 35% | - Persisting transformed data - Complex data pipelines - Filtering, Aggregating, and Joining DataFrames - Window functions - Using built-in functions |
| Topic 3: Snowpark API for Python | 30% | - DataFrame creation and manipulation - Working with Semi-structured data - Reading and writing data - Establishing connections and session management - User-Defined Functions (UDFs) and Stored Procedures |
| Topic 4: Snowpark Concepts | 15% | - Client-side vs. Server-side execution - Snowpark Sessions and connection management - Stored procedures and conditional logic - Snowpark architecture and core concepts - Snowpark DataFrames and query plans - Transformations vs. Actions |
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NEW QUESTION # 205
You are tasked with creating a Snowpark Python stored procedure that reads data from a Snowflake table, performs a complex data transformation using a 3rd party Python library (e.g., pandas, scikit-learn), and writes the transformed data to another Snowflake table.
The data transformation requires significant memory. You need to register this stored procedure in Snowflake. Which of the following approaches is the MOST appropriate for registering the stored procedure and managing the dependencies?
Answer: D
Explanation:
Using a conda environment file ('environment.ymlS) uploaded to a stage is the recommended and most reliable way to manage dependencies for Snowpark Python stored procedures. It ensures that all required libraries and their versions are consistently available. Option A is incorrect as libraries are not pre-installed. Option B is deprecated. Option C is possible but less manageable than a conda environment. Option E is not possible, you cannot directly install onto the warehouse.
NEW QUESTION # 206
You are working with Snowpark DataFrames representing sales transactions. The 'transactions df DataFrame contains recent transactions, while the 'sales_table' in Snowflake holds the historical sales data'. You need to merge the new transactions into the 'sales table', but you want to track which rows were inserted, updated, or left unchanged by the 'merge' operation. How can you capture this information using Snowpark and persist it to a separate table?
Answer: B
Explanation:
The 'returning' clause is a powerful feature of the 'merge' statement in Snowflake SQL. It allows you to capture the rows that were affected by the merge operation, along with details about the type of change (INSERTED, UPDATED, DELETED). In Snowpark, you can leverage this by including a 'returning' clause in your 'merge' statement and then use the returned DataFrame to write the data to a tracking table. This provides a direct and efficient way to monitor the impact of your merge operations. Therefore the correct answer is B.
NEW QUESTION # 207
You are setting up a development environment for Snowpark using Anaconda and encounter the following error: 'ModuleNotFoundError: No module named 'snowflake.snowpark". You have already installed the package using pip. What is the MOST likely cause of this error and how do you resolve it?
Answer: A,E
Explanation:
The most common reasons for 'ModuleNotFoundErroff are that the Anaconda environment is not activated (B), meaning the Python interpreter doesn't know where to find the installed packages, or the package was installed in a different environment (D) than the one being used. While Python version compatibility (A) and Snowflake driver (C) can cause issues, they usually manifest as different errors. A misconfigured account identifier (E) would prevent a connection to Snowflake, but wouldn't directly cause a 'ModuleNotFoundError' for the Snowpark library itself.
NEW QUESTION # 208
You have a Snowpark DataFrame named 'products_df' with columns 'product_id' (INT), 'product_name' (VARCHAR), and 'price' (FLOAT). You want to create a new DataFrame called 'discounted_products df that includes all columns from 'products_df' plus a new column named 'discounted_price', which is calculated as the original price minus a discount percentage specified by the variable 'discount_rate' (e.g., 0.1 for 10%). The 'discount_rate' is stored in the database table named 'discount_table'. You want to load the rate to variable. Choose the correct ways to achieve this. (Select all that apply)
Answer: A,D,E
Explanation:
Options A, B and D provide valid ways to fetch 'discount_rate' as a single numerical value. And fetch the data and gets the first value from the first row. Similarly, gets the data and return the first row. However, Option C does not have LIMIT 1 and will not work. Option E fetches one row as one array, thus requires rate[0] to compute discounted_price.
NEW QUESTION # 209
Given a Snowpark DataFrame 'employees_df with columns 'employee_id' , 'department' , and 'salary' , and a second Snowpark DataFrame 'departments_df with columns 'department_id' and which of the following Snowpark code snippets correctly performs a join to retrieve employee information along with their department name, filtering for employees with salaries greater than $60,000, and then orders the result by department name?
Answer: C
Explanation:
Explanation:Option E first filters the 'employees_df for salaries greater than $60,000, then joins with 'departments_df using the appropriate join condition (employee.department = department.department_id), and finally orders the result by department name. Using instead of and instead of also acceptable syntax. It's more efficient to filter before joining, so E is better than D, and more importantly E and D work correctly. The other options will throw an error.
NEW QUESTION # 210
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