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Free SPS-C01 sample questions

Real questions from the SnowPro Specialty: Snowpark practice bank, with the correct answer and an explanation for each one. No junk, no filler.

Try them in the simulator Same questions, with study, timed and flashcard modes.

Showing 6 of 12 free sample questions.

Question 1Choose one

A data engineer writes a Snowpark Python script that connects to Snowflake, initializes a DataFrame via `df = session.table("SALES")`, applies a filter `df = df.filter(col("AMOUNT") > 1000)`, and adds a new column `df = df.with_column("TAX", col("AMOUNT") * 0.1)`. After executing these lines in a Jupyter Notebook, the engineer checks the Snowflake Query History but sees no queries corresponding to these operations. What is the primary reason for this behavior?

Question 2Choose 2

A data architecture team is migrating legacy ETL pipelines to Snowflake using Snowpark. They have two distinct requirements: 1. Process a column containing a comma-separated string of product codes, parsing it and returning multiple distinct rows for each product code. 2. Create a control-flow orchestration script that sequentially truncates a staging table, executes a MERGE statement, and initiates a task failure alert if exceptions occur. Which TWO Snowpark key objects should the team implement to satisfy these respective requirements? (Select TWO)

Question 3Choose one

When developing a Snowpark application, a data scientist uses the standard Python `math.sqrt()` function within a local for-loop to calculate values before applying them to a DataFrame, as opposed to using `snowflake.snowpark.functions.sqrt()` directly on the DataFrame columns. What is the fundamental architectural difference between these two approaches?

Question 4Choose one

True or False: Third-party Python packages that are not managed by the Snowflake Anaconda repository must be uploaded to a Snowflake stage and added via `session.add_import()` to be utilized within Snowpark server-side objects like UDFs.

Question 5Choose one

A developer is configuring their local development environment to build a Snowpark Python application. They want to ensure absolute compatibility with the packages that will eventually run server-side inside Snowflake's execution environment. Which installation approach is considered the best practice by Snowflake for setting up the Python environment?

Question 6Choose one

A data science team is evaluating development environments for a new ML pipeline using Snowpark. They need an environment that provides native Git integration, advanced debugging, and the ability to work entirely offline for local code authoring before pushing to Snowflake. Which development environment best satisfies these specific requirements?

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