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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are developing a Snowpark Python stored procedure for processing financial data'. The procedure uses the 'pandas' library for data manipulation and the 'scipy' library for statistical calculations. You want to optimize the execution of the stored procedure to leverage the available resources in your Snowflake environment. Which of the following strategies would be MOST effective in improving the performance of your stored procedure, considering the need to handle large datasets?

A) Utilize Snowpark's vectorized UDFs to perform the calculations on the data in parallel, avoiding the need to transfer the data to Pandas.
B) Increase the warehouse size to the largest available option (e.g., X-Large) to provide more memory and CPU resources.
C) Convert the Snowpark DataFrame to a Pandas DataFrame within the stored procedure and perform all calculations using Pandas and SciPy.
D) Partition the input DataFrame into smaller chunks and process each chunk sequentially using Pandas, then combine the results.
E) Use the 'cachetools' library within the stored procedure to cache intermediate results of calculations, reducing redundant computations.

2. You have a Snowflake table 'PRODUCT CATALOG' with columns 'PRODUCT ID, 'PRODUCT NAME, and 'CATEGORY ID. You also have a table 'CATEGORY' with 'CATEGORY ID' and 'CATEGORY NAME. You need to create a Snowpark DataFrame that joins these two tables and includes only 'PRODUCT NAME and 'CATEGORY NAME. Assume a Snowpark Session object named 'session' is available. Which code snippet demonstrates creating the DataFrame using Snowpark's join functionality and column selection while using the 'table' method?

A)

B)

C)

D)

E)

3. 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) Replace the 'Window.orderBy' with 'Window.partitionBV on a highly cardinal column to distribute the window calculations across multiple nodes.
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) Use the method on the Snowpark DataFrame before applying the window function to avoid re-reading the data multiple times.
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.

4. You are working with a Snowpark DataFrame that contains product information including 'product_name' and 'description'. You need to create a new column named 'search_terms' that contains the first three words from the 'description' column, converted to lowercase. If the description has fewer than three words, the 'search_terms' column should contain all the words available. The words should be separated by a space. What is the MOST efficient way to achieve this using Snowpark?

A)

B)

C)

D)

E)

5. You've developed a Snowpark Python UDTF that performs complex data transformation. This UDTF needs to be operationalized within a data pipeline. You want to ensure high performance and scalability. Which of the following strategies will be MOST effective in operationalizing this UDTF?

A) Register the UDTF as a permanent function without any resource allocation and depend on snowflake auto scaling.
B) Deploy the UDTF as an external function hosted on a cloud provider without any resource constraints.
C) Register the UDTF as a temporary function within a single Snowpark session for ad-hoc analysis.
D) Register the UDTF as a permanent function with appropriate resource allocation (warehouse size) and utilize vectorization techniques within the UDTF's implementation.
E) Run the UDTF sequentially on a small dataset to minimize resource consumption.

Solutions: