SPS-C01 Valid Practice Questions | SPS-C01 Certification

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q300-Q305):

NEW QUESTION # 300
You have a Snowflake table named 'raw events' with a VARIANT column named 'event data'. The 'event data' column contains JSON objects with a field 'timestamp' that is sometimes represented as a string and sometimes as a number (Unix epoch). You need to create a Snowpark DataFrame that extracts the 'timestamp' as a timestamp object, handling both string and numeric representations. Which of the following code snippets correctly accomplishes this, avoiding errors when encountering incompatible types?

Answer: A

Explanation:
Option D correctly uses the 'is_number' function to check if the timestamp is numeric. If it is, it divides by 1000 (assuming milliseconds) and converts to a timestamp. If it's not numeric, it converts directly to a timestamp (assuming it is a string representation). Options A, B, C, and E will fail when encountering mixed data types because 'to_timestamp' expects either a number or a string, not both interchangeably. Casting numeric value as String then passing to 'to_timestamp' would raise issues, it needs to be divided.


NEW QUESTION # 301
You are developing a Snowpark application using Visual Studio Code and the Snowflake VS Code extension. You want to configure the extension to automatically detect and use a specific Anaconda environment for your Snowpark development. Assuming you have already created an Anaconda environment named 'snowpark_env', which configuration setting in the VS Code settings.json file would correctly specify the Python path for the Snowflake extension?

Answer: A

Explanation:
Option D is the correct configuration setting. The 'python.defaultlnterpreterPath' setting in VS Code's 'settings.json' file is used to specify the Python interpreter path that VS Code should use for all Python-related tasks, including running and debugging Snowpark applications. Options A and C are incorrect because the Snowflake extension uses standard VS Code Python settings. Option E is for SnowSQL and not directly related to Snowpark Python development within VS Code. The path needs to point to the python executable inside your conda enviornment.


NEW QUESTION # 302
Consider the following Snowpark Python code snippet designed to perform a join operation between two large tables: 'transactions' and 'customers'. The 'transactions' table contains billions of rows and the 'customers' table contains millions of rows. You are experiencing performance bottlenecks during the join operation. The 'transactions' table has a 'customer id' column that references the 'customers' table's primary key 'id'. Which optimization techniques would be MOST effective in improving the join performance within a Snowpark- optimized warehouse?

Answer: B

Explanation:
Broadcasting the smaller table (customers) to all nodes allows for a more efficient join by avoiding the need to shuffle large amounts of data. Snowflake automatically handles index management and optimization; explicitly defining indexes is not necessary. While repartitioning can sometimes improve join performance, in this scenario broadcasting the smaller table is generally more effective. Increasing the warehouse size might help, but it's less targeted than broadcasting. 'how='broadcastr is not a valid parameter in Snowpark's 'join()' function.


NEW QUESTION # 303
You've created a Snowpark Python UDF that uses a third-party library (e.g., scikit-learn) to perform machine learning inference. You need to ensure that this UDF is executed securely and efficiently in Snowflake. Which of the following approaches represent best practices for managing dependencies and securing the UDF environment? Select all that apply.

Answer: C,D

Explanation:
Including the library's code directly into the UDF is not manageable and maintainable. Snowflake's Anaconda channel simplifies dependency management by providing a curated set of packages. Snowflake's managed dependencies and secure execution environment ensure that the UDF runs in a secure and isolated environment. Disabling security is unacceptable. While external functions could manage dependencies, using the built-in Anaconda integration is simpler within Snowflake. It's also more performant than the overhead of an external function call.


NEW QUESTION # 304
You are developing a Snowpark application that utilizes a UDF. You need to ensure that the UDF runs with the privileges of the caller (the user executing the query). Which of the following steps are necessary to accomplish this while creating the Snowpark session?

Answer: A

Explanation:
To ensure a UDF runs with the privileges of the caller, you need to explicitly specify the 'api_caller_identity=sf.Caller.CALLER when defining the UDF using Snowpark. This instructs Snowflake to execute the UDF with the caller's permissions. No special session configurations are needed. Option A is irrelevant for caller's identity. Option B is incorrect as it's not automatic. Option D does not exist. Option E is a valid SQL command but needs to be implemented in Python using 'session.sqr function


NEW QUESTION # 305
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