Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are developing a Snowpark Python application that reads a large dataset (1 TB) from a Snowflake table 'TRANSACTIONS and performs complex aggregations. The application is experiencing significant performance issues, with query execution taking several hours. You have already verified that the warehouse size is appropriate and caching is enabled. You suspect the issue might be related to data skew and incorrect partitioning. Which of the following strategies would be MOST effective in identifying and mitigating this performance bottleneck?

A) Use to force a broadcast join, assuming the aggregated data is small enough to fit in memory. Monitor query profiles to confirm the broadcast occurs.
B) Analyze the 'TRANSACTIONS' table's data distribution using and histograms on the join keys. Based on the analysis, use with the most skewed column to redistribute the data more evenly. Also, consider using bucketing if appropriate.
C) Increase the Snowflake warehouse size to the largest available option (e.g., X6-Large) to provide more resources for query execution, without analyzing data distribution.
D) Implement caching using after reading the data from the 'TRANSACTIONS' table and before performing any aggregations.
E) Use partition_expression=sf.rand())' to randomly repartition the DataFrame into 100 partitions, regardless of the data distribution in the ' TRANSACTIONS table.

2. 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) Utilize Snowflake Streams and Tasks to incrementally transform and process the data within Snowflake, leveraging Snowpark UDFs for complex calculations.
C) Implement Snowpipe to continuously load the streaming data into Snowflake tables and use Snowpark DataFrames to process the data incrementally.
D) Increase the virtual warehouse size to an extremely large instance to handle the high volume of streaming data.
E) Write the streaming data directly to external stages and query it using Snowpark DataFrames with external table access.

3. You are building a Snowpark application that uses a Python UDF to perform sentiment analysis on customer reviews. The UDF relies on a large pre-trained machine learning model loaded from a file. During execution, you encounter 'Out of Memory' errors within the UDF. Considering the constraints of the Snowpark execution environment and the need to optimize resource usage, which of the following steps is the MOST effective in addressing this issue and ensuring the application's stability and performance?

A) Break down the customer reviews into smaller chunks and process them in batches within the UDF, clearing the model from memory after each batch to reduce overall memory consumption.
B) Increase the overall size of the Snowflake warehouse to provide more memory for UDF execution. The Snowflake environment will automatically allocate more memory to UDFs when available.
C) Use Snowpark's 'sproc' to register the UDF as a stored procedure instead ofa UDF, as stored procedures typically have more memory allocated to them.
D) Optimize the model itself by reducing it's size through quantization or distillation, and re-upload the smaller model to the Snowflake stage for the UDF to use.
E) Implement lazy loading of the machine learning model within the UDF, ensuring that the model is loaded only when it's first needed, and then cached for subsequent calls within the same UDF invocation.

4. You are developing a Snowpark application that performs several complex transformations on a large DataFrame representing customer purchase history. This DataFrame is used multiple times in the application. You need to optimize the application's performance by caching the DataFrame. Which of the following approaches is the MOST efficient and memory-conscious way to cache the DataFrame in Snowpark?

A) Using immediately after the initial DataFrame creation.
B) Using 'session.createDataFrame(df.toPandas())' to convert the Snowpark DataFrame to Pandas and back to Snowpark DataFrame.
C) Creating a temporary table in Snowflake using and then reading it back into a new DataFrame for each subsequent operation.
D) Using to store the entire DataFrame in a Python list, then creating a new DataFrame from that list for each subsequent operation.
E) Using without specifying a storage level. Snowflake will choose a default storage level.

5. You are developing a Snowpark Python stored procedure that needs to interact with an external REST API. The API requires authentication using an API key, which you want to store securely and access within the stored procedure. What is the MOST secure and recommended way to store and retrieve the API key within the stored procedure?

A) Store the API key in a Snowflake table and query it within the stored procedure.
B) Store the API key in a Snowflake Secret and access it using the 'secrets' module within the stored procedure.
C) Store the API key as an environment variable within the Snowflake warehouse configuration.
D) Store the API key as a constant string within the stored procedure's code.
E) Store the API Key as a comment in the Store procedure code, and retrieve it using REGEX

Solutions: