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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. A data scientist is developing a Snowpark application that needs to authenticate to Snowflake using Key Pair Authentication. Which of the following steps are essential for configuring the Snowflake CLI to enable Key Pair Authentication and then correctly create a Snowpark session? (Select TWO)

A) Set the 'AUTHENTICATOR parameter in the Snowflake CLI configuration to 'snowflake'.
B) Set the 'PRIVATE KEY parameter in the Snowflake CLI configuration to the path of the private key file.
C) Generate an RSA key pair using 'ssh-keygen' and store the private key securely.
D) Configure the user in Snowflake to use the public key by executing 'ALTER USER SET RSA_PUBLIC_KEY="'.
E) Set the 'AUTHENTICATOR parameter in the Snowflake CLI configuration to 'EXTERNALBROWSER.

2. You are developing a Snowpark application that processes high-volume event data stored in a Snowflake table named 'raw events'. The application aggregates data by session ID. You observe significant performance degradation during peak hours. Analyzing Snowflake query history reveals that the 'session_id' column has high cardinality and data skew. Which of the following strategies, or combination of strategies, would be MOST effective in optimizing the aggregation performance?

A) Use a 'GROUP clause in the Snowpark DataFrame to perform the aggregation.
B) Increase the warehouse size to a larger tier (e.g., from X-Small to Small).
C) Use a 'GROUP BY' clause in the Snowpark DataFrame combined with a 'hint' to specify the ' for optimized parallel processing.
D) Pre-aggregate the raw event data into smaller batches using a scheduled task before the main Snowpark application runs, and then aggregate the pre- aggregated data in the Snowpark application.
E) Implement a custom UDF (User-Defined Function) in Python to perform the aggregation and then apply the 'GROUP clause in the Snowpark DataFrame.

3. 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 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()'.
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) 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.
D) Download all the image files to the Snowpark client, process them locally using Pillow, and then upload the extracted metadata to Snowflake using session
E) 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.

4. A data engineering team is developing a Snowpark stored procedure in Python to perform anomaly detection on time-series data stored in a Snowflake table named 'sensor_readingS. The stored procedure needs to efficiently process large volumes of data and return only the rows identified as anomalies. Which of the following approaches would provide the most performant and scalable solution for operationalizing this stored procedure?

A) Use the method to include a pre-trained anomaly detection model (pickled object) in the stored procedure's execution environment. Load the model, use it to predict on the data fetched using 'session.table(Y , and return a Snowpark DataFrame of anomalies.
B) Use the Snowpark API to directly perform anomaly detection calculations (e.g., rolling statistics, z-score calculations) on the 'sensor_readings' table within the stored procedure, leveraging Snowpark's distributed processing capabilities, and then return the resulting Snowpark DataFrame containing only the anomalies.
C) Load the entire 'sensor_readings' table into a Pandas DataFrame within the stored procedure, perform anomaly detection using a Python library like 'scikit-learn' , and then create a Snowpark DataFrame from the filtered Pandas DataFrame to return the results.
D) Execute a SQL query from within the stored procedure using the Snowflake connector for Python to fetch the relevant data, then use a standard Python loop to iterate through the results and apply anomaly detection logic. Return the anomalous rows as a list of dictionaries.
E) Create a UDF with a Scala implementation and use it inside the Snowpark stored procedure to detect anomalies using the Scala implementation for increased processing power.

5. You are building a Snowpark Python application to perform complex data transformations and want to leverage external packages not pre-installed in the Snowflake environment. You need to ensure these packages are available within your Snowpark session. Which of the following methods are valid for deploying and using these third-party packages within your Snowpark Python environment? (Select TWO)

A) Manually install the packages on the Snowflake compute pool nodes before starting the Snowpark session.
B) Create a 'conda' environment file ('environment.yml') specifying the required packages and use the method to upload the environment definition. Snowflake will automatically install the packages within the session's environment.
C) Use the 'session.addDependency()' method to upload individual '.py' files containing the package code directly to the Snowflake internal stage.
D) Utilize Snowflake's Anaconda channel integration and specify the package names as strings in the method. Snowflake will automatically resolve and install the packages from the Anaconda channel.
E) Deploy the required packages using the SnowCLl package management commands, and then the Snowpark session will be able to automatically use the deployed packages.

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