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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q342-Q347):
NEW QUESTION # 342
You have a requirement to process a large number of JSON files stored in a Snowflake stage 'json_stage'. These JSON files contain complex nested structures. You need to extract specific fields from these files using Snowpark Python and load them into a Snowflake table. You want to use 'SnowflakeFile' to read the JSON files and minimize the amount of data loaded into memory. Select all that apply from the following options to efficiently accomplish this task:
Answer: C
Explanation:
Option C is the most efficient approach. - A UDTF allows for parallel processing of the JSON files within the Snowflake environment. - By directly using 'SnowflakeFile' objects, you avoid unnecessary data transfer outside of Snowflake. - The function incrementally parses the JSON data, minimizing memory usage compared to loading the entire file at once. Option A loads all JSON files into a DataFrame which can lead to memory issues with large JSON files. Option B reads all files using snowflake connector in python, so it is not optimal. Option D downloads all files which would be inefficient. Option E returns a JSON string from UDF then parses it again which is redundant and less efficient.
NEW QUESTION # 343
You are optimizing a Snowpark Python application that performs complex data transformations on a large dataset. You notice significant performance bottlenecks. Which of the following optimization techniques would be MOST effective in leveraging the Snowpark architecture to improve performance?
Answer: D
Explanation:
Lazy evaluation (C) is a key optimization strategy in Snowpark. By chaining transformations, Snowpark can optimize the execution plan and push down operations to the Snowflake engine for efficient processing. Converting to Pandas DataFrames (A) brings data out of Snowflake, negating the benefits of the engine. While vectorized UDFs (B) can be useful, they may not always be as efficient as optimized native Snowpark functions. Manual partitioning (D) is usually handled automatically by Snowflake. session.sql() can bypass optimizations available to Snowpark.
NEW QUESTION # 344
Consider the following Snowpark Python code snippet that retrieves data and calculates aggregate values, however, the application performance is slow when you are fetching dataframe, given the 'block' parameter controls the synchronous/asynchronous behavior of the 'collect()' method. Choose ALL the statements about "session.create_dataframe([rowl ,row2],schema)' that are correct:
Answer: D
Explanation:
The method, when used with the default 'block=True' , operates synchronously, meaning it waits for the result to be fully available before proceeding to the next line of code. This can introduce latency and slow down the application, especially with larger datasets. Using 'block=False' makes the operation asynchronous, allowing the application to continue executing other tasks while the data is being retrieved in the background. Increasing warehouse size will have the same effect regardless, because of . The 'block' parameter is crucial for controlling synchronous versus asynchronous behaviour. The warehouse has no direct influence since the bottleneck is with 'collect()'. Converting to pandas does not fix the inherent blocking problem and makes the problem worse, given it increase network I/O to move data to client side.
NEW QUESTION # 345
Consider a scenario where you have a table 'EMPLOYEES' with columns 'employee id', 'department', and 'salary'. You want to delete employees who belong to either the 'HR' or 'Finance' department and have a salary less than 60000. Which of the following Snowpark DataFrame operations correctly implements this deletion?
Answer: E
Explanation:
Option E is the correct solution because it uses the 'delete' function with the correct boolean logic: == 'HR') I (col('department') 'Finance')) & (col('salary') < 60000)'. This accurately translates to 'department is HR OR department is Finance AND salary is less than 60000'. Option A has incorrect syntax for 'delete()' usage. Option B is missing parenthesis for correct precedence. Option C has incorrect precedence; the 'and' will bind tighter than the or. Option D filters twice but does not correctly use the .delete' method with filter conditions.
NEW QUESTION # 346
You are developing a Snowpark Python stored procedure that needs to interact with an external REST API. The API requires authentication using an API key, which you want to store securely and access within the stored procedure. What is the MOST secure and recommended way to store and retrieve the API key within the stored procedure?
Answer: D
Explanation:
Option D is the MOST secure and recommended approach. Snowflake Secrets provide a secure way to store sensitive information like API keys. They are encrypted and managed by Snowflake, reducing the risk of unauthorized access. The 'secrets' module allows you to retrieve these secrets within your stored procedures without exposing them in the code. Option A is the least secure because the API key would be directly visible in the stored procedure's code. Option B is slightly better than A, but still poses a risk of exposure if the table is compromised. Option C is not a valid option as you cannot define environment variables within the Snowflake warehouse configuration. Option E is the worst option as anyone with access to view the stored procedure definition can readily see the API Key. Further, Comments can be accidentally printed in logs.
NEW QUESTION # 347
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