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| Section | Weight | Objectives |
|---|---|---|
| Snowpark API for Python | 30% | - Working with Semi-structured data - Establishing connections and session management - User-Defined Functions (UDFs) and Stored Procedures - Reading and writing data - DataFrame creation and manipulation |
| Snowpark Concepts | 15% | - Snowpark architecture and core concepts - Snowpark DataFrames and query plans - Snowpark Sessions and connection management - Stored procedures and conditional logic - Client-side vs. Server-side execution - Transformations vs. Actions |
| Performance Optimization and Best Practices | 20% | - Debugging and explain plans - Vectorized UDFs - Warehouse sizing for Snowpark - Query pushdown and optimization - Caching strategies - Minimizing data transfer |
| Data Transformations and DataFrame Operations | 35% | - Complex data pipelines - Using built-in functions - Persisting transformed data - Filtering, Aggregating, and Joining DataFrames - Window functions |
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NEW QUESTION # 20
Consider the following Snowpark code snippet that aims to calculate the rank of each employee based on their salary within their respective department. What are potential issues with this code, and how can you improve it? (Select all that apply.)
Answer: B,C,E
Explanation:
Options B, D, and E are correct. B: The 'orderBy' clause needs the 'col' function call in D: Null values in the salary column can lead to unexpected ranking results, and should be addressed beforehand. E: Applying window functions on very large DataFrames can be resource-intensive, so filtering data beforehand can improve performance. Option A is incorrect because there are indeed issues with the code. Option C: 'rank()' function does not produce dense ranks. function is used for contiguous ranks.
NEW QUESTION # 21
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 # 22
You have a complex data pipeline implemented using Snowpark Tasks in a Directed Acyclic Graph (DAG). One of the tasks, , depends on the successful completion of two parent tasks, and 'task B'. You need to implement error handling such that if 'task_R fails, 'task_C' should not be executed, but should still complete its execution regardless of status. If 'task B' fails, 'task_C' should not be executed. How do you configure the task dependencies and error handling in Snowflake to achieve this behavior?
Answer: D
Explanation:
Setting to execute 'AFTER task_A, task_B' establishes the dependency. Configuring on both 'task_R and 'task_B' ensures that if either task fails, it will suspend itself and prevent 'task_C' from running. This achieves the required behavior. Option A is incorrect because setting the parameter on 'task_C' will not prevent the execution of 'task_C' if either 'task_R or failed, but only suspend it after it tries to execute and fails. Option B is not correct because Snowflake's default behavior does not inherently skip 'task_C' unless specifically configured through dependencies. Option C is overly complex and doesn't accurately reflect intended behaviour.Option E is overly complex and doesn't accurately reflect intended behaviour. Option D will ensure only if both 'task_A' and runs successfully then runs else it will suspend the further execution.
NEW QUESTION # 23
A data scientist has developed a complex machine learning model in Python that needs to be operationalized within a Snowpark pipeline. This model depends on several custom Python packages not available in Snowflake's default environment. The data scientist wants to define a UDTF to apply this model to incoming data'. Which of the following steps are NECESSARY to successfully deploy and execute this UDTF in Snowflake? (Select three)
Answer: A,B,C
Explanation:
To deploy a UDTF with custom Python packages, you need to: 1 . Isolate the required packages using a virtual environment. 2. Upload the entire virtual environment (or a selection) as a ZIP file to a Snowflake stage, to make it available to Snowflake. 3. Reference the stage location of the ZIP file in the 'imports clause of the 'CREATE FUNCTION' statement. Options A, C and E are necessary for the UDTF to access the packages. Option B is not required and can cause issue, if entire virtual environment is not packaged appropriately. Option D is not recommended, if entire vitual environment is packaged. It is possible, packages are dependant on some python internal modules.
NEW QUESTION # 24
You are profiling a Snowpark application that uses a combination of SQL queries and Python UDFs. You observe that a particular stage involving a UDF is taking significantly longer than expected. You suspect that the UDF's performance is the bottleneck. Which of the following steps would be the MOST comprehensive approach to diagnose and address the performance issue?
Answer: A
Explanation:
Option B offers the most structured and informed approach. The query profile provides detailed insights into execution times for each stage, including UDF execution. Analyzing the UDF code then allows for targeted optimization. While A, C, and D are potentially helpful, they are less systematic. E is premature without proper diagnosis. The query profile in Snowflake is the most comprehensive and targeted approach to the performance troubleshooting. Also it is important to understand the code inside UDF.
NEW QUESTION # 25
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