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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark application that reads data from a large Snowflake table and performs several transformations. During testing, you observe that the application's performance is inconsistent, with some runs taking significantly longer than others, even with the same input data'. You suspect that data locality might be a contributing factor. What steps can you take within your Snowpark application to investigate and potentially improve data locality and performance consistency?

A) Disable Snowflake's result cache. This ensures that the application always reads the most recent data from disk, regardless of performance impact.
B) Ensure the Snowpark session is configured with a large enough warehouse size to minimize data spilling to disk.
C) Implement caching using , combined with a targeted 'repartition()' to ensure that frequently accessed data is readily available in memory close to the processing nodes.
D) Use to redistribute the data across the cluster based on a relevant key. This can improve data locality for subsequent operations.
E) Enable Snowflake's automatic clustering on the underlying table if it's not already enabled. This will physically organize the data on disk based on the clustering key.

2. Consider the following Snowpark code snippet that defines and registers a UDF:

Which of the following statements about this code are TRUE?

A) The default value of 'salutation' in the Python function will be used even when calling the UDF from SQL if the salutation parameter is omitted.
B) The 'input_types' parameter is redundant because Python's type hints are automatically used to determine the input types.
C) The 'replace=True' argument ensures that any existing UDF with the same name ('ADD_SALUTATION') is overwritten.
D) The UDF is registered as a permanent UDF and stored in the specified stage for future use.
E) The UDF is registered as a temporary UDF and will be removed when the session ends.

3. You are working with a Snowpark DataFrame named containing information about products, including 'CATEGORY , 'SUBCATEGORY , and 'PRICE'. You want to determine the maximum price for each subcategory within each category. Furthermore, you need to filter the results to only include categories that have more than 5 subcategories. Which of the following Snowpark Python code snippets accomplishes this task? (Select all that apply)

A)

B)

C)

D)

E)

4. Consider the following Snowpark Python code snippet:

A) This code will ovemrite the table if it already exists.
B) This code will fail because is not a valid method for Snowpark DataFrames.
C)

D) The code will fail because there is no call to or on the 'result_df dataframe and Snowflake performs lazy evaluation.
E) The 'result_df DataFrame will be persisted to the 'AGGREGATED SALES table in the default schema of the user running the code.

5. You are developing a Snowpark Python application that processes streaming data from an external source. The application requires near real-time insights and involves complex data transformations. However, you are observing high latency in the data processing pipeline. Which of the following optimization techniques would be MOST relevant to address this issue in the context of Snowpark and Snowflake?

A) Pre-aggregate the streaming data using an external stream processing engine (e.g., Apache Kafka, Apache Flink) before loading it into Snowflake.
B) Increase the virtual warehouse size to an extremely large instance to handle the high volume of streaming data.
C) Write the streaming data directly to external stages and query it using Snowpark DataFrames with external table access.
D) Implement Snowpipe to continuously load the streaming data into Snowflake tables and use Snowpark DataFrames to process the data incrementally.
E) Utilize Snowflake Streams and Tasks to incrementally transform and process the data within Snowflake, leveraging Snowpark UDFs for complex calculations.

Solutions: