Snowflake SPS-C01 actual dump : Snowflake Certified SnowPro Specialty - Snowpark

You have a Snowpark Python application that reads data from a Snowflake table named 'SALES DATA', performs several transformations using DataFrames, and then writes the results back to a new table named 'AGGREGATED SALES'. The application runs successfully, but you notice that the write operation to 'AGGREGATED SALES' is consistently slow. After examining the query profile, you observe significant skew in the data being written, causing some worker nodes to be overloaded. Which of the following techniques could you use within your Snowpark application to mitigate the data skew and improve the write performance to 'AGGREGATED SALES'?

• A. Implement custom partitioning logic using a User-Defined Function (UDF) that calculates a hash value based on the skew key and then uses the 'DataFrame.repartitionByRange(col)' method to partition the data based on the hash values.
• B. Increase the size of the Snowflake warehouse being used to execute the Snowpark application. This will provide more compute resources to handle the data skew.
• C. Use the 'DataFrame.sort(col)' method to sort the data by the skew key before writing it to 'AGGREGATED SALES'. This will ensure that rows with similar values are processed by the same worker node.
• D. Use the method to specify a clustering key on the 'AGGREGATED SALES' table during table creation. This will physically organize the data on disk based on the skew key, improving write performance.
• E. Use the method to evenly redistribute the data across a larger number of partitions before writing it to 'AGGREGATED SALES'.

You are developing a Snowpark Python application that connects to Snowflake using key pair authentication. You have the private key stored securely in an environment variable named 'SNOWFLAKE PRIVATE KEY. Which of the following code snippets correctly establishes a Snowpark session using this method, assuming all other necessary connection parameters (account, user, database, schema, warehouse) are also set as environment variables?

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You are working with a data science team that needs to create Snowpark DataFrames from various file types (CSV, JSON, Parquet, and XML) stored in different locations (internal stages, external stages on AWS S3, and Azure Blob Storage). The team wants a unified and reusable function to create DataFrames, abstracting away the specific file format and location details. Which of the following approaches using Snowpark Python API will provide the MOST flexible and maintainable solution?

• A. Create a class hierarchy with an abstract base class 'DataFrameReader' that defines a 'read_file' method. Implement subclasses for each file format and location, overriding the 'read_file' method with the specific logic for that format and location.
• B. Create a generic function str, file_format: str, options: dicty that uses 'getattr(session.read, file format)' to dynamically call the appropriate 'session.read' method based on the 'file_format' parameter. Pass additional configuration through the 'options' dictionary.
• C. Use the 'session.sqr method with dynamically generated SQL queries that include the file format and location details. Construct the SQL query string based on the input parameters.
• D. Implement a single function that uses a series of 'if/elif/else' statements to determine the file type and location, then calls the appropriate 'session.read' method with the corresponding options.
• E. Create separate functions for each file type and location combination (e.g.,

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. Increase the warehouse size to a larger tier (e.g., from X-Small to Small).
• B. Use a 'GROUP clause in the Snowpark DataFrame to perform the aggregation.
• C. Implement a custom UDF (User-Defined Function) in Python to perform the aggregation and then apply the 'GROUP clause in the Snowpark DataFrame.
• 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. Use a 'GROUP BY' clause in the Snowpark DataFrame combined with a 'hint' to specify the ' for optimized parallel processing.

You are working with a Snowpark DataFrame containing customer data, including a 'phone_number' column. Some phone numbers are missing or have incorrect formats. You want to impute missing values with a default phone number '000-000-0000' and remove any phone numbers that do not match the pattern 'XXX-XXX-XXXX' using Snowpark Python. Which of the following code snippets achieves this most efficiently?

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