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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are working with a Snowpark DataFrame representing sensor data. The DataFrame contains columns like 'timestamp', 'sensor id' , and 'value'. You need to perform a complex windowing operation to calculate the moving average of the 'value' for each 'sensor id' over a 5-minute window, but only for data points where the 'value' is greater than a threshold. The window should be defined based on the 'timestamp' column. What is the most efficient and correct approach to implement this using Snowpark DataFrames?

A) Use a loop to iterate over each 'sensor_id' , filter the DataFrame for that sensor, calculate the moving average using Pandas windowing functions, and then combine the results.
B) First apply the moving average calculation to the DataFrame and then filter for rows with values exceeding the threshold, since calculations are performed in order.
C) First, collect the entire DataFrame into a Pandas DataFrame, then use Pandas windowing functions to calculate the moving average.
D) Use a combination of 'filter' to apply the threshold condition, 'Window.partitionBy' and 'Window.orderBy' to define the window, and 'avg' window function to calculate the moving average.
E) Create a UDF that takes a list of timestamps and values as input and returns the moving average. Apply this UDF to the entire DataFrame.

2. You've created a Snowpark Python stored procedure designed to perform sentiment analysis on customer reviews stored in a Snowflake table. This procedure utilizes a third-party Python library, 'transformers', for its sentiment analysis model. You need to operationalize this stored procedure for scheduled execution. Which of the following options represents the MOST efficient and reliable approach for deploying and managing the 'transformers' dependency within the Snowflake environment for your stored procedure, minimizing deployment complexity and potential runtime errors?

A) Upload the 'transformers' library as a zip file to a Snowflake stage and reference it in the 'imports' parameter when creating the stored procedure.
B) Install the 'transformers' library on the Snowflake compute warehouse nodes directly using a startup script.
C) Create a Snowflake Anaconda channel, upload the 'transformers' library to this channel, and specify the channel in the stored procedure definition using the packageS parameter.
D) Include the 'transformers' library directly within the stored procedure's Python code as a string literal and execute it using or 'eval()'.
E) Include the 'transformers' library as a part of the task definition and make it available for the stored procedure that the task is invoking.

3. You have configured your Snowpark application to use a '.env' file for storing connection parameters. The ' .env' file contains the following:

Which of the following code snippets demonstrates the most secure and recommended method for creating a Snowpark session using these environment variables and also ensuring the file exists?

A)

B)

C)

D)

E)

4. You are tasked with optimizing the performance of a Snowpark application that uses a UDF to perform complex image processing. The UDF is currently registered using 'session.udf.registeff. You observe that the UDF execution is slow, particularly when processing large batches of images. What steps could you take to potentially improve the performance of this UDF execution? Select all that apply.

A) Ensure that the size of the zipped file containing the UDF's dependencies is as small as possible to speed up deployment.
B) Increase the warehouse size used for running the Snowpark application to provide more computational resources for the UDF execution.
C) Ensure that the stage location specified during UDF registration is in the same region as the Snowflake account to minimize data transfer latency.
D) Convert the UDF into a UDTF (User-Defined Table Function) if the image processing can benefit from row-by-row processing and aggregation.
E) Use session.clear_packages() to clear the session packages when deploying UDF.

5. You have a Python function that takes a string as input and returns a sentiment score (a float between -1 and 1). This function relies on a large pre-trained Natural Language Processing (NLP) model. You want to deploy this function as a UDF in Snowpark and optimize its performance, specifically minimizing the model loading time for each execution. You have already uploaded the model to a stage named '@my_stage/models'. Select the option that combines caching techniques and UDF deployment strategies to achieve the best performance.

A)

B) Option B and C are best and gives a scalable solution
C)

D)

E)