Snowflake Certified SnowPro Specialty - Snowpark - SPS-C01

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are optimizing a Snowpark application that performs complex data transformations on a large dataset. The transformation involves multiple joins and aggregations. You notice that the query execution time is excessive. Which of the following techniques would be MOST effective in improving the performance of this application, assuming you have the appropriate Snowflake role and privileges?

A) Reduce the overall complexity of the query, but increase the number of queries being executed. This allows for a more incremental approach.
B) Use the ' DataFrame.explain(Y method to analyze the query plan and identify potential bottlenecks. Optimize the code based on the query plan analysis, focusing on reducing data shuffling and improving join strategies.
C) Increase the size of the Snowflake warehouse used for the computation. A larger warehouse provides more resources for parallel processing.
D) Disable result caching for the Snowflake session. This ensures that the most up-to-date data is always used, even if it slows down performance.
E) Rewrite the Snowpark code to use more UDFs (User-Defined Functions). UDFs are always more efficient than built-in functions.

2. You are tasked with creating a Snowpark session that utilizes a specific Snowflake warehouse for all operations. Which of the following code snippets BEST demonstrates how to correctly specify the 'warehouse' parameter when creating a session using snowpark.Session.builder.configs'?

A)

B)

C)

D)

E)

3. You're tasked with loading data representing transactions from a legacy system into Snowflake using Snowpark. The legacy system exports the transaction data as a Python list of tuples, where each tuple contains transaction ID (integer), transaction amount (float), and transaction date (string in 'YYYY-MM-DD' format). The scale of data can be very high and need optimized way to load the data'. Your goal is to create a Snowpark DataFrame from this list of tuples, ensuring the date column is correctly interpreted as a Snowflake Date type. Which of the following approaches would be the most efficient and correct, minimizing data conversion overhead and maximizing Snowpark's capabilities?

A) Create a list of dictionaries from the list of tuples with correct column names, and define a Snowpark schema using 'StructType' and 'StructField', explicitly setting the data type of the date column to 'DateType'. Then, create the Snowpark DataFrame using 'session.createDataFrame(data, schema=schema)'.
B) Convert the list of tuples to a Pandas DataFrame, explicitly specifying the column names and data types (including 'pd.datetime64[ns]' for the date column). Then, create a Snowpark DataFrame from the Pandas DataFrame using 'session.createDataFrame(pandas_df)'.
C) Create a Snowpark DataFrame directly from the list of tuples using 'session.createDataFrame(datay , relying on automatic schema inference. No need to explicitly convert to 'DateType' as Snowflake will take care of implicit conversion.
D) Define a Snowpark schema using 'StructType' and 'StructField' , explicitly setting the data type of the date column to 'DateType'. Then, create the Snowpark DataFrame using 'session.createDataFrame(data,
E) Create a Snowpark DataFrame directly from the list of tuples using 'session.createDataFrame(data)' , relying on automatic schema inference. Then, use function to cast the date column to a DateType.

4. You are working with image files stored in a Snowflake internal stage named 'image_stage'. You need to write a Snowpark Python application to resize these images using a Python library called 'PIG. The resizing logic is encapsulated in a function called resize_image(snowflake_file: SnowflakeFile, width: int, height: int) -> bytes. Which of the following code snippets correctly registers he 'resize image' function as a UDF and applies it to the image files?

A)

B)

C)

D)

E)

5. You are working with a Snowpark DataFrame called 'customer df that contains customer data, including a column named 'registration_date' of data type TIMESTAMP NTZ. You need to filter the DataFrame to only include customers who registered in the year 2023. Which of the following Snowpark code snippets represents the MOST efficient and correct way to accomplish this filtering, considering potential timezone issues?

A)

B)

C)

D)

E)

Solutions: