Snowflake SPS-C01 Dumps [2026]–SPS-C01 Exam Questions

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q104-Q109):

NEW QUESTION # 104
You have a Snowpark DataFrame containing customer transaction data'. Your goal is to save this DataFrame as a set of Parquet files in an existing Snowflake stage named , partitioned by the 'transaction_date' column. You want to ensure that the files are automatically compressed using the Zstandard codec and that existing files with the same name are overwritten. Which of the following Snowpark code snippet achieves this with the most optimal approach and respects best practices?

Answer: B

Explanation:
Option A correctly uses the 'parquet' method directly for writing Parquet files to a stage. It specifies partitioning by 'transaction_date', overwrites existing files using , and sets the compression codec to 'zstd' using the 'option' method. The 'saveAsTable' method, used in option B & E, is intended for creating or overwriting tables, not writing files to a stage. Option D uses a fully qualified Snowflake URL to save the DataFrame, but using saveAsTable is not for writing files into stage . The 'option('fileFormat', 'parquet')' in option C is not the most direct way to specify the format; using .parquet()' is more concise and idiomatic.


NEW QUESTION # 105
You have a large CSV file containing product descriptions that you need to analyze using a sentiment analysis UDF. The CSV file is too large to fit in memory on your local machine. You want to stream the data directly from a Snowflake stage to your UDF for processing, avoiding the need to download the entire file. Which of the following approaches allows you to achieve this using Snowpark?

Answer: D

Explanation:
Option B is the correct approach. Creating an external table allows you to treat the CSV file on the stage as a regular table within Snowflake. You can then use a Snowpark DataFrame to query this external table and efficiently process the data in parallel using the sentiment analysis UDF. Option A is not feasible because you cannot directly load the file into Pandas, as it requires the file to be local to UDF execution environment, defeating the purpose of using a stage directly. Option C: is not intended for reading arbitrary files; it's typically used for Snowpipe ingestion. Option D introduces unnecessary data movement and complexity. Option E is discouraged due to security concems and potential resource conflicts; UDFs should not establish new connections to Snowflake.


NEW QUESTION # 106
You are tasked with optimizing a Snowpark Python application that performs complex data transformations on a large dataset. The application is running slower than expected, and you suspect that data serialization and transfer between the Snowpark client and the Snowflake engine are bottlenecks. Which of the following strategies could you implement to improve performance? (Select all that apply.)

Answer: A,D,E

Explanation:
Options A, B, and C are correct strategies. Pushing down computation (A) reduces data transfer. Using smaller batch sizes (B) can reduce memory pressure, especially for large datasets. Using temporary tables (C) allows intermediate results to be stored and processed entirely within Snowflake, avoiding unnecessary data transfer. Option D is incorrect because converting to Pandas DataFrames brings the data to the client, negating the benefits of Snowpark's distributed processing. Option E is dangerous since it could cause bottleneck if the resources are not managed correctly.


NEW QUESTION # 107
You are using Snowflake Notebooks to develop a Snowpark application and want to leverage a custom Python library that is not available in the default environment. What steps are necessary to make this library available within your Snowflake Notebook?

Answer: C

Explanation:
Snowflake Notebooks primarily use conda environment specification files ('environment.yml') (B) to manage dependencies. You specify the required libraries in the 'environment.ymr file, upload it to a stage, and use it when creating or updating the Notebook environment. Uploading raw .pV files (A) might work for simple modules, but lacks dependency management. Using '!pip install' (C and E) directly in the notebook is not the intended way to manage dependencies in Snowflake Notebooks for production scenarios and might not persist across sessions.


NEW QUESTION # 108
You have a Snowpark DataFrame representing customer transactions. This DataFrame is used in multiple downstream operations within your Snowpark application. Which of the following strategies would be MOST effective for optimizing the performance of these downstream operations by materializing the results of the 'df DataFrame, and what considerations should be made regarding resource usage?

Answer: C,D

Explanation:
Using materializes the DataFrame in memory, which is faster for repeated access but requires sufficient memory. Creating a temporary table using temporary=TrueV persists the DataFrame to Snowflake storage, reducing recomputation at the cost of storage 1/0. Choosing between these options depends on the DataFrame's size, available memory, and the frequency of access. Writing to a persistent table adds unnecessary overhead unless persistence is required. Using a local variable will only persist the result within the scope of that variable, not across multiple Snowpark operations. Checkpointing is used for lineage truncation not caching.


NEW QUESTION # 109
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