100% Pass 2026 Snowflake Professional Authorized SPS-C01 Pdf

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q294-Q299):

NEW QUESTION # 294
A data engineering team is developing a Snowpark stored procedure to perform complex data transformations and load the results into a target table. They want to operationalize this procedure by scheduling it to run daily. Which of the following is the MOST reliable and scalable way to schedule the execution of this Snowpark stored procedure within Snowflake?

Answer: D

Explanation:
Snowflake Tasks are the recommended way to schedule stored procedures within Snowflake. They are a native Snowflake feature, providing scalability, reliability, and integration with Snowflake's monitoring and management tools. Airflow is a valid option, but adds external dependencies.


NEW QUESTION # 295
You are developing a Snowpark application that needs to read data from a set of CSV files stored in a Snowflake stage named ' my_stage'. The files have a header row and are comma-delimited. You want to use the Snowpark API to create a DataFrame from these files, automatically inferring the schema. Which of the following code snippets correctly achieves this?

Answer: B

Explanation:
Option D is the correct way to read CSV files from a stage in Snowpark with header and schema inference using .format("csv").option(...y. Option A is close, but it doesn't use the correct syntax for specifying the stage location; it expects a file path relative to the current working directory.Option B incorrectly uses .csv()' with directly.Option C correctly passes options, but must use the load method in conjunction with format option. Option E has incorrect path specification in addition to incorrect function chain. Option D uses .load()" after setting the format and options is most appropriate.


NEW QUESTION # 296
You have a Snowpark DataFrame named containing daily sales transactions. The DataFrame includes columns like 'transaction_id' , 'product id', 'sale_date', and 'sale_amount'. You need to perform the following transformations and persist the results: (1) Calculate the total sales amount for each product on a daily basis. (2) Store the aggregated results into a new table named , partitioned by 'sale_date'. (3) Ensure that if the table already exists, the new data is appended to the existing table. Which of the following code blocks achieve these requirements in the most efficient and correct manner?

Answer: A

Explanation:
Option A is the most efficient and correct. It first aggregates the data as required, then uses 'mode('append')' to add new data without overwriting existing data, and for partitioning. Option B will overwrite existing data. Option C is equivalent to option A, but is a less common coding style. Option D renames the default aggregate column name which is less readable but also works correctly. Option E, 'mergeSchema' isn't a valid option to be passed.


NEW QUESTION # 297
You have a Snowpark DataFrame containing sales data with columns 'sale_date', and 'sale_amount'. You need to calculate the cumulative sales amount for each product over time, ordered by 'sale_date'. Which of the following Snowpark code snippets correctly implements this using window functions?

Answer: A

Explanation:
Option A is correct. It correctly uses to group by 'product_id' and 'order_by' to sort by 'sale_date' within each product group. It then calculates the cumulative sum using Options B, C, D and E contain typos or incorrect function usage or order of arguments. 'cumulative_surn' is not a standard function provided.


NEW QUESTION # 298
You are developing a Snowpark application that processes real-time streaming data'. The application needs to perform a complex calculation for each incoming event. To improve performance, you decide to leverage asynchronous execution and User-Defined Functions (UDFs). However, you are encountering issues with the order of results and ensuring that the processing order matches the arrival order of the events. Which of the following strategies MOST effectively addresses the challenge of maintaining processing order while leveraging asynchronous execution and UDFs in Snowpark?

Answer: B

Explanation:
Asynchronous execution, by definition, doesn't guarantee the order of completion. To maintain order, a custom ordering mechanism based on a timestamp or sequence number is necessary. Storing the results in a temporary table and sorting them before further processing is a viable approach. Synchronous calls (A, D) negate the benefits of asynchronous execution. Snowflake's internal optimization (C) doesn't guarantee order in asynchronous scenarios. Reimplementing the calculation without UDFs (E) might not be feasible or efficient if the calculation is complex. The 'block' parameter is relevant to asynchronous behaviour and must be False' to truly utilize the asynchronous execution model. The UDFs will be executed asynchronously, and the order is maintained by using Timestamp.


NEW QUESTION # 299
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