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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowpark Concepts | 15% | - Snowpark architecture and core concepts - Snowpark DataFrames and query plans - Transformations vs. Actions - Client-side vs. Server-side execution - Snowpark Sessions and connection management - Stored procedures and conditional logic |
| Topic 2: Data Transformations and DataFrame Operations | 35% | - Window functions - Filtering, Aggregating, and Joining DataFrames - Persisting transformed data - Complex data pipelines - Using built-in functions |
| Topic 3: Snowpark API for Python | 30% | - Working with Semi-structured data - Reading and writing data - User-Defined Functions (UDFs) and Stored Procedures - Establishing connections and session management - DataFrame creation and manipulation |
| Topic 4: Performance Optimization and Best Practices | 20% | - Debugging and explain plans - Minimizing data transfer - Caching strategies - Warehouse sizing for Snowpark - Query pushdown and optimization - Vectorized UDFs |
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NEW QUESTION # 135
You have a Snowpark Python stored procedure that reads data from a Snowflake table, performs a complex calculation using Pandas, and then writes the results back to another Snowflake table. You are experiencing performance issues, and you suspect the data transfer between Snowpark and Pandas is a bottleneck. Which of the following techniques could significantly improve the performance of this stored procedure? (Select two)
Answer: B,C
Explanation:
Options B and D are the most effective. Vectorized operations (B) significantly speed up Pandas calculations. Performing transformations within Snowflake (D) avoids unnecessary data transfer between Snowpark and Pandas, reducing the bottleneck. A is useful, but secondary. C only affects Snowflake side processing, but it may help. E would be useful, but not as helpful as pushing as much as possible down to Snowflake processing.
NEW QUESTION # 136
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 # 137
You are using Snowpark Python to build a machine learning pipeline. One step in the pipeline involves feature engineering using a large dataset. This feature engineering step is computationally expensive and involves several transformations. You want to optimize the performance of this step by caching intermediate results. Given the following code snippet, which of the following strategies would be MOST effective for optimizing the performance, considering the use of
Answer: D
Explanation:
The most effective strategy is to cache DataFrames that are reused multiple times. Caching the initial raw data before any transformations might not be beneficial if the transformations significantly reduce the data size. Caching every intermediate DataFrame, even those used only once, adds unnecessary overhead. Avoiding entirely is not optimal, as caching can significantly improve performance when used strategically. Caching only the final DataFrame is useful if the entire feature engineering process needs to be reused, but it doesn't optimize the individual steps within the process. Caching is most useful at points where derived data sets (i.e. after heavy calculations) are used repeatedly.
NEW QUESTION # 138
You have a Snowpark DataFrame containing sales data with columns 'sale_date', and 'sale_amount'. You need to calculate the cumulative sales amount for each product over time, ordered by 'sale_date'. Which of the following Snowpark code snippets correctly implements this using window functions?
Answer: C
Explanation:
Option A is correct. It correctly uses to group by 'product_id' and 'order_by' to sort by 'sale_date' within each product group. It then calculates the cumulative sum using Options B, C, D and E contain typos or incorrect function usage or order of arguments. 'cumulative_surn' is not a standard function provided.
NEW QUESTION # 139
You have a Snowpark DataFrame named 'employee_df with columns 'employee_id', 'department', and 'salary'. You want to calculate the average salary for each department and add it as a new column named 'avg_department_salary' to the original DataFrame. Additionally, you want to sort the resulting DataFrame by department and then by salary in descending order. Which of the following Snowpark code snippets correctly implements this requirement?
Answer: D,E
Explanation:
Option D and E are correct. Both implement the window function correctly to add the average salary per department as a new column and then sort the DataFrame as required. Option E is very close and just has the window partition defined with 'col('department')'. Option A is correct but uses sort. Option B is incorrect because it does not include the original columns other than department when grouping so will loose important data, and attempts to sort on a column that no longer exists.Option C uses 'avg('salary')' instead of avg(col('salary'))
NEW QUESTION # 140
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