Snowflake SPS-C01 Actual Tests : Snowflake Certified SnowPro Specialty - Snowpark

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. 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)

A) Use the 'TABLE function in Snowpark to directly access the source table instead of reading the entire table into a Snowpark DataFrame at once.
B) Utilize vectorized operations within Pandas to minimize explicit looping and improve calculation speed.
C) Increase the warehouse size to the largest possible value before executing the stored procedure.
D) Leverage Snowpark's optimized functions and UDFs wherever possible to perform transformations within Snowflake's engine instead of transferring data to Pandas.
E) Convert the Pandas DataFrame to a Dask DataFrame for distributed computation.

2. You are developing a Snowpark application to process large datasets. You want to leverage asynchronous jobs to improve performance and prevent blocking the main thread. You have the following code snippet:

A) Call 'job.result(Y without any error handling. Snowflake will automatically handle any errors and return a null result.
B) Use 'job.getQueryld(Y to fetch the query ID and query the execution status directly from Snowflake using SQL. This is inefficient and bypasses the Snowpark API.
C) Implement a callback function using 'job.on_success(callback_function)' and 'job.on_error(error_function)' to handle the result or any errors asynchronously.
D) Continuously check 'job.status' in a loop until it returns 'SUCCESS', then retrieve the result using 'job.result()'. This is less efficient due to polling.
E) Use to retrieve the result with a timeout of 30 seconds, catching 'TimeoutError' if the job takes too long. This is the only way to get the result safely.

3. You are developing a Snowpark application to process images stored in an internal stage. You have defined a Python UDF to detect objects in each image using a pre-trained model. The UDF takes the image file path as input and returns a JSON string containing the detected objects and their bounding boxes. However, you encounter "SerializationError' when running the UDF. Which of the following steps are MOST likely to resolve this issue effectively, assuming the model itself is correctly loaded and functions within the UDF environment?

A) Serialize the output of the UDF (the JSON string) using a custom serialization function that handles complex data types appropriately, and deserialize it in the Snowpark DataFrame.
B) Reduce the size of the images before passing them to the UDF to reduce memory consumption and serialization overhead. Resize images before ingesting them.
C) Increase the value of the 'MAX MEMORY USAGE parameter for the warehouse to provide more memory for UDF execution. This will prevent running out of resources when processing large images.
D) Convert the image file path to the image file content using a Snowpark function such as 'snowpark.functions.read' before passing it to the UDF.
E) Ensure that the Python environment used for UDF execution has the 'pillow' library installed by specifying it in the 'imports' parameter of the 'create_udf function with the corresponding packages for loading and preprocessing images.

4. You have a DataFrame 'df containing user profile data'. A column named "profile" stores JSON objects with potentially missing fields. These objects might include 'name', 'age', 'location', and 'preferences'. You need to extract the user's name and age. If 'age' is missing, you want to default to 0. Furthermore, you want to filter out any rows where the 'location' field is an empty string. Which combination of Snowpark code snippets will achieve this? (Select all that apply)

A)

B)

C)

D)

E)

5. Consider a Snowflake table 'orders' with columns 'order_id', 'customer_id', 'order_date', and 'status'. You need to update the 'status' of all orders placed before January 1, 2023, to 'Archived'. Which of the following approaches is the most efficient and idiomatic way to achieve this using Snowpark DataFrames, assuming 'orders df DataFrame represents the 'orders' table?

A) Option C
B) Option B
C) Option A
D) Option D
E) Option E

Solutions: