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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Objectives
Topic 1: Testing, Debugging, and Deployment	- Production readiness 1. Deployment strategies 2. Debugging Snowpark applications
Topic 2: DataFrame Operations and Data Processing	- Data transformation workflows 1. Filtering, selecting, and aggregations 2. Joins and window functions
Topic 3: Data Engineering with Snowpark	- Pipeline development 1. Batch processing workflows 2. Integration with Snowflake data pipelines
Topic 4: User Defined Functions and Stored Procedures	- Extending Snowpark with custom logic 1. Stored procedures in Snowpark 2. Python UDFs
Topic 5: Performance Optimization and Best Practices	- Efficient Snowpark execution 1. Resource utilization tuning 2. Pushdown optimization concepts
Topic 6: Snowpark Fundamentals	- Snowpark architecture and concepts 1. Snowflake execution model overview 2. Snowpark APIs and supported languages

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q297-Q302):

NEW QUESTION # 297
Consider the following Snowpark Python code snippet designed to calculate the moving average of sales data'. You've identified that the code is performing poorly and suspect the window function is a bottleneck. How can you optimize this code for better performance?

A. Use the method on the Snowpark DataFrame before applying the window function to avoid re-reading the data multiple times.
B. Explicitly specify a range-based window frame (e.g., 'rowsBetweeri) instead of a rows-based window frame (e.g., 'rangeBetweeri) if appropriate for the calculation.
C. Replace the 'Window.orderBy' with 'Window.partitionBV on a highly cardinal column to distribute the window calculations across multiple nodes.
D. Ensure that the data is pre-sorted according to the ordering specified in the window function before creating the Snowpark DataFrame.
E. Rewrite the window function logic as a series of aggregation queries to improve performance on very large datasets.

Answer: A,E

Explanation:
Caching the DataFrame allows reuse of the data and avoids recomputation, improving performance. Rewriting the logic with aggregation queries is a viable optimization. Partitioning by a cardinal column does not improve performance. Presorting the data before creating the DataFrame does not affect Window function performance. Range-based windows are not always a direct replacement and have specific use cases.

NEW QUESTION # 298
You have a Snowpark DataFrame with columns 'department' , and 'salary'. You want to identify employees in each department whose salary is within the top 20% of salaries for that department. Which of the following approaches, using window functions, is the MOST efficient way to achieve this?

A. Use the 'ntile(5)' window function to divide each department's employees into 5 buckets based on salary, then select employees in the top bucket.
B. Calculate the average salary per department, then filter employees whose salary is greater than 80% of the average salary.
C. Calculate the maximum salary per department, then filter employees whose salary is greater than or equal to 80% of the maximum salary.
D. Use window function to rank employees within each department by salary, then calculate the 80th percentile salary using a separate aggregation and join back to the original DataFrame to filter.
E. Use the window function to calculate the percentile rank of each employee's salary within their department, then filter for ranks greater than or equal to 0.8.

Answer: E

Explanation:
Option B is the most efficient. directly calculates the percentile rank, allowing for a simple and efficient filter. Options A and C only consider the average or maximum salary and don't provide a percentile rank. Option D divides into 5 buckets (quintiles), which isn't precise enough for identifying the top 20%. Option E is less efficient as it involves multiple steps: ranking, aggregation, and joining.

NEW QUESTION # 299
You have a Snowpark application that utilizes a vectorized Python UDF to perform complex calculations on a large dataset. You notice that the performance is still not optimal. You suspect that the bottleneck might be related to how the data is being partitioned and processed by Snowflake. Which of the following actions, when performed in conjunction with vectorization, would MOST likely improve performance?

A. Broadcast the DataFrame to all compute nodes before applying the UDF.
B. Ensure that the data is pre-sorted according to the primary key of the table before applying the UDF.
C. Repartition the Snowpark DataFrame using to align the data distribution with the computational needs of the UDF.
D. Convert the DataFrame to a Pandas DataFrame before applying the UDF.
E. Increase the number of UDF worker threads within the UDF definition.

Answer: C

Explanation:
Repartitioning the DataFrame using allows you to control how the data is distributed across compute nodes. This can improve performance by ensuring that related data is processed together, reducing data shuffling and improving data locality. Pre- sorting data (A) might help in some cases, but it doesn't guarantee optimal data distribution for parallel processing. Broadcasting the DataFrame (C) is suitable for smaller datasets, not large ones where it can lead to memory issues. Converting the DataFrame to a Pandas DataFrame (D) defeats the purpose of using Snowpark for distributed processing and introduces a single-node bottleneck. There's no direct control over the number of UDF worker threads in Snowflake.

NEW QUESTION # 300
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: B,D

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 # 301
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)

A. Specify the stage location in the 'imports' clause of the 'CREATE FUNCTION' statement when defining the UDTF.
B. Upload the ZIP file to a Snowflake stage.
C. Include only custom packages into the ZIP file and exclude common python library packages, as snowpark is pre-installed.
D. Package the virtual environment as a ZIP file.
E. Create a virtual environment and install all the required Python packages.

Answer: A,B,E

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 # 302
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