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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Python dictionary 'data' representing configuration settings for your Snowpark application. You need to convert this dictionary into a Snowpark DataFrame with a single row and two columns named 'Setting' and 'Value'. The 'Setting' column should contain the keys from the dictionary, and the 'Value' column should contain the corresponding values. The DataFrame needs to be created efficiently and ensure string representation of both the setting and value. Which approach is most suitable, ensuring correctness and conciseness?

A) 'python settings = [{'Setting': k, 'Value': v} for k, v in data.items()] df = session.createDataFrame(settings)
B) python settings = [1k, str(v)] for k, v in data.items()] schema = ['Setting', 'Value'] df = session.createDataFrame(settings, schema=schema)
C) python import snowflake.snowpark.types as T settings = list(data.items()) schema = T.StructType([T.StructField('Setting', T.StringType()), T.StructField('Value', T.StringType())]) df = session.createDataFrame(settings, schema=schema)
D) python import pandas as pd pd_df = pd.DataFrame(data.items(), columns=['Setting', 'Value')) df = session.createDataFrame(pd_df)
E) 'python settings = [1k, v] for k, v in data.items()] df = session.createDataFrame(settings, schema=['Setting', 'Value'])

2. 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?

A) Use synchronous UDF calls with a large Snowflake warehouse to minimize processing time and guarantee order.
B) Employ asynchronous UDF calls with 'block-False' and rely on Snowflake's internal optimization to maintain the processing order.
C) Utilize asynchronous UDF calls with 'block-False' and implement a custom ordering mechanism based on a timestamp or sequence number associated with each event. Store the results in a temporary table and sort them based on the timestamp before further processing.
D) Use synchronous UDF calls with a small Snowflake warehouse to introduce artificial delays and ensure order.
E) Abandon the use of UDFs altogether and reimplement the complex calculation using only built-in Snowpark DataFrame transformations to ensure order.

3. You have written a Snowpark Python function that performs a complex calculation involving user-defined functions (UDFs). When running this function on a large dataset, you encounter a 'PicklingError: Can't pickle ': it's not the same object as main.my function'. What is the MOST likely cause of this error, and how can you resolve it?

A) The UDF is defined within a local scope or closure, and Snowpark cannot serialize it. Move the UDF definition to the global scope or use 'cloudpickle' explicitly.
B) The Snowpark session is not properly initialized. Ensure that the connection parameters are correct.
C) The UDF's return type is not correctly specified. Use 'udf(func, to explicitly define the return type.
D) The dataset is too large to be processed in memory. Use 'df.cache()' to persist the intermediate results to disk.
E) The UDF contains unsupported Python libraries. Ensure that all dependencies are available on the Snowflake worker nodes.

4. 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?

A)

B)

C)

D)

E)

5. You have two Snowpark DataFrames, 'dfl' and 'df2, representing customer data'. 'dfl' contains columns 'CUSTOMER ID', 'NAME, and 'EMAIL', while 'df2 contains 'CUSTOMER ID' and 'PURCHASE AMOUNT'. You need to create a new DataFrame that combines the information from both DataFrames but only includes customers who exist in BOTH 'dfl ' and 'df2 and the resulting DataFrame should have columns from both. Which of the following Snowpark DataFrame operations should you use, and what is the correct way to call it?

A)

B)

C)

D)

E)

Solutions: