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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have two Snowpark DataFrames: containing customer information and 'orders_df containing order details. You need to merge these DataFrames based on the column to create a unified view. The 'customers_df may contain duplicate values. The contains recent orders. You want to use 'merge' with an 'UPDATE action based on 'customer id' and a 'WHEN NOT MATCHED BY TARGET action to insert new customer records from 'customers df into the 'orders df table that do not exist.

A) The 'UPDATE action in 'merge' will update all matching rows in the target table based on the join condition, regardless of duplicates in the source.
B) The 'merge' operation will succeed, processing each matching record sequentially. If more than one row matches it will process based on order in the dataframe
C) You must use on the source DataFrame ('customers_df) before using 'merge' to ensure only one matching row exists per customer.
D) The 'merge' operation will fail immediately upon encountering the first duplicate key during the update operation. Consider stage the source dataframe and use an appropriate dedup option with merge.
E) The merge operation cannot handle duplicate keys in the source DataFrame without pre-aggregation.

2. You have a Snowpark application that reads data from a large Snowflake table and performs several transformations. During testing, you observe that the application's performance is inconsistent, with some runs taking significantly longer than others, even with the same input data'. You suspect that data locality might be a contributing factor. What steps can you take within your Snowpark application to investigate and potentially improve data locality and performance consistency?

A) Disable Snowflake's result cache. This ensures that the application always reads the most recent data from disk, regardless of performance impact.
B) Use to redistribute the data across the cluster based on a relevant key. This can improve data locality for subsequent operations.
C) Ensure the Snowpark session is configured with a large enough warehouse size to minimize data spilling to disk.
D) Enable Snowflake's automatic clustering on the underlying table if it's not already enabled. This will physically organize the data on disk based on the clustering key.
E) Implement caching using , combined with a targeted 'repartition()' to ensure that frequently accessed data is readily available in memory close to the processing nodes.

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

4. A data engineering team is building a Snowpark pipeline to process IoT sensor data'. They want to create a UDF that uses a 3rd-party Python library (not available in Snowflake's Anaconda channel) to analyze the sensor readings. The UDF needs to be efficiently deployed and managed within Snowflake. Which of the following approaches represents the MOST robust and scalable way to register and deploy this UDF using Snowpark?

A) Use 'session.udf.register' and directly include the library code as a string within the UDF definition. This avoids external dependencies.
B) Create a Docker container with the Python library, push it to Snowflake Container Services, and call this container from the UDF.
C) Use 'session.add_packages' to add the specific Python package directly from the Snowflake Anaconda channel (even if the required version isn't available) and then use 'session.udf.register' for the UDF definition.
D) Create a virtual environment with the necessary Python library, zip it, upload the zip file to a Snowflake stage, and use to register the UDF. Reference the stage location and virtual environment in the register call.
E) Use 'functions.udf and directly embed the package code within the UDF definition. This approach handles package management automatically.

5. A Snowpark application connects to Snowflake using key pair authentication. After several successful executions, the application starts failing with authentication errors. You suspect an issue with the private key. Considering best practices for security and troubleshooting, which of the following actions should you take FIRST to diagnose and resolve the problem?

A) Immediately revoke the user's access to Snowflake and create a new user with a different private key.
B) Verify that the private key file exists at the specified path in the Snowpark application configuration and that the user running the application has read permissions on the file.
C) Restart the Snowpark application server to refresh the session and clear any cached credentials.
D) Rotate the public/private key pair immediately and update the Snowpark application with the new private key. Generate new username and password.
E) Check the Snowflake login history in ACCOUNT _ USAGE view to identify the specific error message and the IP address from which the failed login attempts originated. Use new authtype and password.

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