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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. A Snowpark Python application is experiencing significant performance degradation when processing a large dataset (100GB+) stored in Snowflake. The application performs a complex series of transformations, including window functions and joins with smaller lookup tables. You suspect data skew is contributing to the issue. Which of the following strategies would be MOST effective in mitigating the impact of data skew and improving performance?

A) Implement salting or pre-partitioning of the data based on a hash of the skewed column before performing the joins and window functions.
B) Disable query result caching to ensure fresh data is always used.
C) Convert the Snowpark DataFrame to a Pandas DataFrame before performing transformations.
D) Cache the smaller lookup tables using 'session.createDataFrame' and broadcast them to all worker nodes.
E) Increase the warehouse size to a larger instance (e.g., from X-Small to Large).

2. You have a Snowflake stage containing image files. You need to write a Snowpark Python application that extracts metadata (e.g., image resolution, format) from these images and stores the metadata in a Snowflake table. You want to leverage a Python library, such as Pillow (PIL), for image processing. Which of the following steps are necessary to correctly and efficiently implement this?

A) Create a Python UDF (User-Defined Function) that uses Pillow to extract metadata from the image files. Register the UDF with Snowflake. In a Snowpark DataFrame transformation, call the UDF for each image file to extract the metadata. Finally, write the resulting DataFrame to a Snowflake table.
B) Use Snowpark's built-in image processing functions to extract metadata directly from the image files. This eliminates the need for external libraries like Pillow.
C) Download all the image files to the Snowpark client, process them locally using Pillow, and then upload the extracted metadata to Snowflake using session
D) Upload the Pillow library as a zip file to a Snowflake internal stage. Create a Snowpark stored procedure. In the stored procedure code, import the Pillow library using 'import zipfile; sys.path.append('pillow.zip'); from PIL import Image'. Read the image files using , process them with Pillow to extract metadata, and then insert the metadata into the Snowflake table.
E) Create a Conda environment specification file ('environment.yml') that includes Pillow as a dependency. Upload the 'environment.ymr file to a Snowflake stage. Use 'session.add_packages' in the Snowpark session to load the Pillow library. Read the image files using , process them with Pillow, and then write the metadata to a Snowflake table using 'session.write_pandas()'.

3. You are using Snowpark Python to build a machine learning pipeline. One step in the pipeline involves feature engineering using a large dataset. This feature engineering step is computationally expensive and involves several transformations. You want to optimize the performance of this step by caching intermediate results. Given the following code snippet, which of the following strategies would be MOST effective for optimizing the performance, considering the use of

A) Avoid using altogether because it can introduce overhead and is not always beneficial.
B) Cache each intermediate DataFrame after each individual transformation step, even if the DataFrame is only used once.
C) Identify DataFrames that are reused multiple times and cache them using after the transformations that generate them.
D) Cache the final DataFrame only after all feature engineering steps are completed.
E) Cache the initial raw data DataFrame before applying any transformations.

4. You are developing a secure UDF in Snowpark Python that needs to access sensitive data stored in an internal stage. The UDF should be accessible to users without granting them direct access to the stage. Which of the following security measures and code snippets are required to achieve this, assuming the stage is already created?

A) Create a secure UDF and use the function to access stage credentials within the UDF's handler function.
B) Create a secure UDF and use a stored procedure owned by a role with access to the internal stage to retrieve data, passing the data to the UDF as an argument.
C) Create an external function and grant access to the API integration that provides the security context.
D) Create a UDF and grant USAGE on the stage to the role that owns the UDF.
E) Create a secure UDF using the ' VOLATILE keyword, allowing it to access secured data with current user's permissions.

5. You need to create a Snowpark DataFrame using a SQL query. The query requires a user-defined variable (e.g., a date for filtering records). What are the correct and recommended ways to safely pass this variable into the SQL query when creating the DataFrame using 'session.sql()' to prevent SQL injection vulnerabilities?

A)

B)

C)

D)

E)

Solutions: