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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark DataFrame containing semi-structured data in a column named 'payload'. The 'payload' column contains JSON objects, and some of these objects contain nested arrays. You need to flatten all arrays, regardless of their level of nesting, and extract specific fields from the flattened data'. What is the MOST efficient approach using Snowpark to achieve this while minimizing the amount of code?

A) Iteratively apply the 'explode' function to each array field within the 'payload' column, manually identifying and flattening each level of nesting.
B) Create a stored procedure in Snowflake that recursively flattens the JSON, then call this stored procedure from Snowpark to transform the DataFrame.
C) Use a single ' SELECT statement with multiple 'LATERAL FLATTEN' calls (using SQL syntax within 'session.sql') to flatten all nested arrays simultaneously.
D) Convert the DataFrame to an RDD, then use the RDD's 'flatMap' function to flatten the nested arrays before converting back to a DataFrame.
E) Use recursive UDFs to traverse and flatten the JSON structure, then create a new DataFrame from the flattened data.

2. Consider the following Snowpark Python code snippet for creating a stored procedure:

What is the PRIMARY reason for explicitly defining 'input_types' and during the stored procedure registration?

A) To enable the stored procedure to be called from other programming languages besides Python.
B) To allow Snowsight to correctly display the stored procedure's metadata, making it easier for users to understand its functionality.
C) To improve the performance of the stored procedure by enabling compile-time optimizations.
D) To ensure data type safety and schema validation during deployment and execution, preventing unexpected runtime errors due to type mismatches between the stored procedure and the calling environment.
E) To allow Snowflake to automatically generate documentation for the stored procedure's input and output types.

3. You have a Snowpark application that performs machine learning inference on a large dataset of images stored in Snowflake. The inference logic is implemented within a Python UDF that utilizes a pre-trained deep learning model. You notice that the inference process is slow and consumes a significant amount of resources. Which of the following optimization techniques would be MOST effective in improving the performance and reducing the resource consumption of this application?

A) Optimize the UDF code to use batch processing techniques to process multiple images in a single call, reducing the overhead of UDF invocation.
B) Use external functions instead of UDFs.
C) Ensure the virtual warehouse used by Snowpark is configured with auto-scaling to dynamically adjust the compute resources based on the workload.
D) Leverage the Snowpark Session object to manage the lifecycle and scope of the pre-trained model within the UDF, ensuring it is loaded only once per session.
E) Persist the pre-trained deep learning model in a database table or stage and load it into the UDF's memory during each invocation to avoid repeated loading.

4. Consider the following Snowflake SQL statement intended to modify the properties of a Snowpark-optimized virtual warehouse named
'SNOWPARK_WH':

Which of the following statements accurately describe the expected outcome of executing this SQL statement?

A) The SQL statement will execute successfully only if the user executing it has the 'MODIFY privilege on the 'SNOWPARK_WH' warehouse.
B) The SQL statement will execute successfully after checking if 'SNOWPARK_WH' is of Snowpark-optimized warehouse type, resizing the 'SNOWPARK_WH' warehouse to 'LARGE', setting the maximum number of clusters to 3, the minimum to 1, and enabling the 'ECONOMY' scaling policy.
C) The SQL statement will fail because the 'SCALING POLICY parameter cannot be set for Snowpark-optimized warehouses.
D) The SQL statement will execute successfully, resizing the 'SNOWPARK WI-i' warehouse to 'LARGE', setting the maximum number of clusters to 3, the minimum to 1, and enabling the 'ECONOMY' scaling policy.
E) The SQL statement will fail because you cannot modify 'WAREHOUSE_SIZE and 'MAX CLUSTER COUNT in a single SALTER WAREHOUSE statement.

5. You are developing a Snowpark stored procedure to perform sentiment analysis on customer reviews. You need to use the 'nltk' Python package, which is not a built-in package in Snowflake. You have already created a stage named 'my_stage' in Snowflake and uploaded the necessary nltk data files (e.g., 'vader_lexicon.zip') to the stage. Which of the following code snippets correctly configures the session and imports the required nltk components within the stored procedure?

A)

B)

C)

D)

E)

Solutions: