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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Performance and Best Practices | 10% | - Optimization techniques
|
| Topic 2: Snowpark API and Development | 30% | - Multi-language support
|
| Topic 3: Snowpark Concepts and Architecture | 25% | - Snowpark architecture and execution model
|
| Topic 4: Data Transformations and Operations | 35% | - DataFrame manipulation
|
>> SPS-C01 Exam Lab Questions <<
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NEW QUESTION # 173
You are tasked with optimizing a Snowpark application that processes sensor data'. The data includes timestamp, sensor ID, and sensor reading. Your initial implementation uses a regular Python UDF to calculate the moving average for each sensor. However, the processing time is significantly slow due to the large volume of data'. Which of the following strategies would be MOST effective in improving the performance of this calculation using vectorization?
Answer: A
Explanation:
Converting the Python UDF to a vectorized UDF allows it to process data in batches (as Pandas Series), which significantly reduces the overhead of transferring data between Snowflake and the UDE While increasing warehouse size (C) can provide some performance gain, vectorization (B) directly addresses the inefficiency of processing individual rows. Using built-in aggregation (D) is also a good option if feasible, but if a custom moving average calculation is required, vectorized UDF is the best fit. SQL UDFs aren't always faster and don't inherently vectorize. Java UDFs may provide some improvement but are more complex to implement than vectorized Python UDFs.
NEW QUESTION # 174
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: C,E
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 # 175
You are designing a Snowpark application to process streaming data ingested into Snowflake using Snowpipe. The application needs to apply a complex set of transformations and aggregations to the incoming data in real-time. Which of the following approaches would be MOST suitable for this scenario, leveraging the strengths of Snowpark architecture?
Answer: B
Explanation:
Using a Snowpark Stored Procedure triggered by a Snowflake Task provides the best solution for real-time processing of streaming data from Snowpipe. The Task automates the execution of the stored procedure whenever new data is available, and the stored procedure leverages Snowpark's server-side capabilities to perform the transformations and aggregations efficiently within the Snowflake environment. Option A involves client-side processing, Option B isn't compatible with Snowpipe directly and chained UDFs may not optimal for complex transformations and aggregations. Option C involves constant writes and may have performance issues with large datasets. While option E could work, utilizing Snowpark stored procedures provides better flexibility and Python code integration for more complex logic.
NEW QUESTION # 176
You have a Snowpark DataFrame named 'products' with columns 'product_id' (INT), 'product_name' (STRING), and 'price' (DOUBLE). You want to apply a transformation to calculate a 'discounted_price' column, which is the 'price' reduced by 10% if the price is greater than $100.00. Which of the following code snippets is the most efficient way to achieve this using Snowpark Python?
Answer: C,E
Explanation:
The most efficient ways are B and C. Option B directly uses the 'when' and 'otherwise' functions in Snowpark, which are optimized for execution within Snowflake. Option C is similar to B but explicitly uses 'lit' to represent the numeric literal, ensuring proper type handling in Snowpark. IJDFs (Option A) are generally less efficient than built-in functions. Option D attempts to use RDDs, which are not part of the Snowpark API. Option E is not valid Snowpark python syntax. Therefore, B and C are the correct answers.
NEW QUESTION # 177
You have a Snowpark DataFrame named and want to create a stored procedure that calculates the average purchase amount for each customer. The stored procedure should accept the DataFrame as input, perform the aggregation, and return a new DataFrame with the results. Which of the following code snippets BEST demonstrates how to correctly define and deploy this stored procedure?
Answer: B
Explanation:
Option E is the most correct. It correctly registers the stored procedure using 'session.sproc.register' with the correct return type annotation for a DataFrame (using TableType and StructFields) and explicitly declares the input type to be DataFrame using 'input_types' parameter in registration. Option A uses the '@sproc' decorator which is the older API (still valid, but less explicit). Option B uses session.sql which is not appropriate for creating DataFrame based stored procedures. Option C is not correct because the return type SeqType(FloatType()) expects a sequence of float types instead of a DataFrame structure. Option D writes the result to a table, which is not the defined requirement to return a DataFrame.
NEW QUESTION # 178
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