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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark Python application that reads data from a Snowflake table named 'SALES DATA', performs several transformations using DataFrames, and then writes the results back to a new table named 'AGGREGATED SALES'. The application runs successfully, but you notice that the write operation to 'AGGREGATED SALES' is consistently slow. After examining the query profile, you observe significant skew in the data being written, causing some worker nodes to be overloaded. Which of the following techniques could you use within your Snowpark application to mitigate the data skew and improve the write performance to 'AGGREGATED SALES'?

A) Increase the size of the Snowflake warehouse being used to execute the Snowpark application. This will provide more compute resources to handle the data skew.
B) Use the method to evenly redistribute the data across a larger number of partitions before writing it to 'AGGREGATED SALES'.
C) Use the 'DataFrame.sort(col)' method to sort the data by the skew key before writing it to 'AGGREGATED SALES'. This will ensure that rows with similar values are processed by the same worker node.
D) Use the method to specify a clustering key on the 'AGGREGATED SALES' table during table creation. This will physically organize the data on disk based on the skew key, improving write performance.
E) Implement custom partitioning logic using a User-Defined Function (UDF) that calculates a hash value based on the skew key and then uses the 'DataFrame.repartitionByRange(col)' method to partition the data based on the hash values.

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 allow Snowsight to correctly display the stored procedure's metadata, making it easier for users to understand its functionality.
B) 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.
C) To enable the stored procedure to be called from other programming languages besides Python.
D) To improve the performance of the stored procedure by enabling compile-time optimizations.
E) To allow Snowflake to automatically generate documentation for the stored procedure's input and output types.

3. A data engineer is tasked with creating a Snowpark session using JWT authentication. They have a private key 'rsa_key.pff, a user name 'snowpark_user' , and an account identifier 'my_account'. The goal is to create a session object suitable for submitting Snowpark jobs. Which code snippet correctly demonstrates the instantiation of a session object using JWT?

A)

B)

C)

D)

E)

4. You are tasked with optimizing a Snowpark application that uses a Python UDF to perform complex string manipulations on a large dataset. The current implementation uses a scalar UDF. You are considering converting it to a vectorized UDF. What are the key considerations and potential limitations you need to address during the conversion to ensure correctness and optimal performance? Choose all that apply:

A) The vectorized UDF's return type must be compatible with Snowpark's data types, and the UDF should return an array of the appropriate type with the same length as the input arrays.
B) The vectorized UDF should utilize libraries like NumPy or Pandas for efficient array processing, but it's important to be aware of the limitations on available Python packages in the Snowflake environment.
C) The input and output data types of the vectorized UDF must exactly match the corresponding column data types in the Snowpark DataFrame.
D) The vectorized UDF must be able to handle NULL values gracefully within the input arrays, as these can cause errors if not explicitly addressed.
E) Vectorized UDFs always perform better than scalar UDFs, regardless of the complexity of the string manipulations or the size of the dataset.

5. You are developing a Snowpark application that processes large volumes of JSON data from an external stage. Initial testing on a MEDIUM warehouse results in significant query queuing. You suspect the issue is CPU bound due to complex JSON parsing and UDF execution within Snowpark. Considering only warehouse sizing options and assuming cost is a secondary concern to performance during peak processing hours, which strategy is MOST effective for optimizing performance? Consider the impact on concurrency.

A) Scale out to multiple MEDIUM warehouses using auto-scaling. This increases concurrency, allowing more queries to run simultaneously, but might not address CPU-bound operations within a single query.
B) Upgrade the warehouse to a LARGE. This provides more CPU and memory for the existing workload, potentially resolving the bottleneck and improving overall throughput.
C) Scale down to a SMALL warehouse. Smaller warehouses are optimized for smaller operations and can process certain types of operations faster. This could improve latency.
D) Implement query acceleration using materialized views to pre-compute JSON parsing results. Then, add warehouses as needed for concurrent requests
E) Upgrade to an X-LARGE or higher warehouse, leveraging the increased resources to handle complex parsing and UDF execution more efficiently. Monitor CPU utilization after the upgrade.

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