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| Section | Objectives |
|---|---|
| Topic 1: User Defined Functions and Stored Procedures | - Extending Snowpark with custom logic
|
| Topic 2: Snowpark Fundamentals | - Snowpark architecture and concepts
|
| Topic 3: Data Engineering with Snowpark | - Pipeline development
|
| Topic 4: Testing, Debugging, and Deployment | - Production readiness
|
| Topic 5: Performance Optimization and Best Practices | - Efficient Snowpark execution
|
| Topic 6: DataFrame Operations and Data Processing | - Data transformation workflows
|
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NEW QUESTION # 109
You are developing a Snowpark application to process customer sentiment from text reviews. You have a Python function, , that utilizes a pre-trained NLP model loaded from a file on a Snowflake stage named This function returns a sentiment score (float) between -1 and 1. You need to register this function as a UDF so that it can be used within Snowpark DataFrames. Which of the following code snippets correctly registers the UDF, ensuring the NLP model is available to the function during execution?
Answer: D
Explanation:
Option E correctly uses the '@udf decorator with the 'imports' parameter to specify the location of the pickled model on the stage. It also uses to correctly construct the path to the imported file within the UDF's execution environment. Replace=True prevents errors if the UDF already exists. Options A, C and D don't correctly handle importing the NLP model. Option B has a security issue of loading file without validating the Path.
NEW QUESTION # 110
You are developing a Snowpark application that performs complex data transformations on a large dataset using a UDF written in Scala.
After deploying the application, you observe that the performance is significantly slower than expected. Analyzing the query history in Snowflake, you identify that the UDF execution time is unusually high. Which of the following actions would be MOST effective in improving the performance of the UDF, considering Snowpark's execution context and Snowflake's query processing?
Answer: B,C
Explanation:
Using vectorized UDFs allows processing data in batches, significantly reducing overhead. Returning smaller datatypes optimizes I/O and memory usage. While increasing the warehouse size might offer some improvement, it doesn't directly address the UDF's inefficiency. Snowpark session memory is more relevant for the driver program and less so for the execution of the UDF within Snowflake's environment. SQL Stored procedures are useful but for functions already supported in SQL; vectorized UDFs provide a path fomard for scala code.
NEW QUESTION # 111
You have two Snowpark DataFrames, 'dfl' and 'df2, representing customer data'. 'dfl' contains columns 'CUSTOMER ID', 'NAME, and 'EMAIL', while 'df2 contains 'CUSTOMER ID' and 'PURCHASE AMOUNT'. You need to create a new DataFrame that combines the information from both DataFrames but only includes customers who exist in BOTH 'dfl ' and 'df2 and the resulting DataFrame should have columns from both. Which of the following Snowpark DataFrame operations should you use, and what is the correct way to call it?
Answer: B,E
Explanation:
To include only customers present in BOTH DataFrames and include columns from both, you need to perform an INNER JOIN. Options B, C and D are incorrect: intersect, union and subtract operations work at a row level. Also, intersect , union and subtract operations expects the number of columns and datatypes to match and is not relevant to the described scenario. Option A and E are valid way to use the join operation: (A) uses the explicit condition 'dfl .CUSTOMER_ID df2.CUSTOMER_lD while (E) is the short form which specifies the column name directly. Both achieves the same inner join behavior. You can choose E as a cleaner option when only joining on column name.
NEW QUESTION # 112
A Snowpark application needs to process large volumes of sensor data stored in a Snowflake table named , which includes columns , 'timestamp' , and The application must calculate a rolling average of for each over a 5-minute window. The data is not perfectly ordered by 'timestamp' within each 'sensor_id'. What is the MOST efficient and accurate way to implement this rolling average calculation using Snowpark?
Answer: C
Explanation:
Option D is the most efficient and accurate. 'partitionBy('sensor_id')' ensures that the rolling average is calculated separately for each sensor. 'orderBy('timestamp'Y orders the data within each partition by timestamp. 0)' defines the 5- minute window relative to the current row, accurately capturing all readings within that window even if they are slightly out of order. 'avg(Y then efficiently calculates the average within that window. Other options are either less efficient (e.g., UDTF iteration) or less accurate (e.g., incorrect window definitions, filtering).
NEW QUESTION # 113
You have two Snowpark DataFrames: 'employees_df with columns 'employee_id' (INTEGER), 'employee_name' (STRING), 'department_id' (INTEGER), and 'salaries_df' with columns 'employee_id' (INTEGER), "salary' (FLOAT), 'effective_date' (DATE). You need to create a new DataFrame that contains the employee's name, department, and the highest salary they have ever received. Assuming there can be multiple salary entries for the same employee with different 'effective date' values, which of the following Snowpark code snippets would correctly and efficiently solve this problem?
Answer: A
Explanation:
Option B is the most efficient solution. It first calculates the maximum salary for each employee in the DataFrame using 'groupBy' and 'max' , then joins this aggregated result with the 'employees_df to retrieve the employee's name and department. This approach avoids unnecessary data shuffling and minimizes the amount of data processed in the join. Option A performs the join before the aggregation, which can be less efficient. Options C and E use window functions, which are more complex and may not be as efficient for this simple aggregation. Option D uses a UDF and 'collect_list' , which can be very inefficient due to data transfer and UDF overhead.
NEW QUESTION # 114
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