Practice SPS-C01 Test & Intereactive SPS-C01 Testing Engine

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q241-Q246):

NEW QUESTION # 241
You are developing a Snowpark application to perform complex data transformations on a large dataset stored in Snowflake. You need to optimize the application's performance. Which of the following strategies are MOST effective for improving performance within Snowpark?

Answer: A,E

Explanation:
Snowpark's optimized functions (B) are designed to be executed efficiently within the Snowflake engine. Lazy evaluation (E) allows Snowpark to optimize the entire query plan before execution. UDFs (A) can be useful, but should be used judiciously as they can introduce overhead. Materializing intermediate DataFrames (C) can increase storage costs and may not always improve performance. 'collect()' (D) should be avoided for large datasets as it moves data out of Snowflake.


NEW QUESTION # 242
You are developing a Snowpark stored procedure to perform sentiment analysis on customer reviews. You need to use the 'nltk' Python package, which is not a built-in package in Snowflake. You have already created a stage named 'my_stage' in Snowflake and uploaded the necessary nltk data files (e.g., 'vader_lexicon.zip') to the stage. Which of the following code snippets correctly configures the session and imports the required nltk components within the stored procedure?

Answer: A

Explanation:
Option E correctly adds the zipped nltk data file from the stage as an import, and then updates the NLTK_DATA environment variable to point to the /tmp directory where Snowflake unpacks the zip file. This ensures that nltk can find its data files. Option A and D attempt to use './nltk_data' which is incorrect as the file system is read-only, and Option B incorrectly uses sys.path.append and session.add_packages in wrong way as well as hard coded path. Option C fails as well with session.add_packages('snowflake-snowpark-python','nltk') incorrect syntax .


NEW QUESTION # 243
A Snowpark application processes streaming data from Kafka, performing complex windowing aggregations. The application is configured with auto-scaling enabled for the virtual warehouse. During peak hours, the application exhibits high latency despite the warehouse scaling up. Upon investigation, you observe sustained high CPU utilization on the single active warehouse. Which actions, alone or in combination, would MOST effectively improve performance while minimizing cost?

Answer: B,C

Explanation:
Optimizing the Snowpark code (Option B) reduces the overall CPU load, while repartitioning the data (Option C) distributes the work more evenly across multiple clusters, reducing hotspots and enabling parallel processing. Increasing MAX_CLUSTER_COUNT (Option A) only helps if the workload is already evenly distributed. Increasing MIN_CLUSTER COUNT (Option D) may add to cost without addressing the underlying code issues or data skew. Decreasing SCALING_POLICY (Option E) may help with responsiveness but isn't a fundamental solution.


NEW QUESTION # 244
You have a Snowpark Python UDTF named that performs complex data transformations and you want to share it securely with another Snowflake account. Select ALL the necessary steps and considerations to properly share this UDTF using Snowflake Secure Data Sharing.

Answer: A,C,D,E

Explanation:
To share a UDTF, you first create a share and grant USAGE on the database containing it (A). Then, you create the UDTF as SECURE to ensure data is protected (B). SELECT is needed to allow the share to actually use the function (C). It is important to make sure that the receiving account will be able to run the UDTF, so ensure all of the needed packages and dependendies are accounted for (D). GRANT OWNERSHIP would mean you would not be able to make changes to the function (E).


NEW QUESTION # 245
Consider the following Snowpark Python code snippet designed to read data from a Snowflake table, apply a user-defined function (UDF) for data transformation, and then write the transformed data to another table. The UDF, 'calculate_score' , requires a configuration file ('config.json') to be loaded. Which of the following code snippets demonstrates the CORRECT and MOST efficient way to load and access the "config.json' file within the UDF, ensuring that it's available to all UDF invocations without requiring network access?

Answer: B

Explanation:
Option E is the most correct answer, leveraging the 'imports' parameter to efficiently load the 'config.json' file from the specified stage into the UDF's environment. The file is then accessed using a relative path ('config.json'), which is the location where Snowflake places the imported file. Option A is incorrect because it attempts to load from '/tmp/config.json' , which is not accessible within the UDF's environment. Option B is incorrect due to it being Permanent UDF and trying to access the session which are mutually exclusive. In addition , the stage location must be at the time of registration rather than in the UDF definitition, which isn't right. Option C is almost right but incorrect because it accesses file with 'os.path.join(os.getcwd(), 'config.json'Y which is unecessary and wrong. Option D is incorrect because files specified in the 'imports' parameter are available in the current working directory directly, no need to import and declare a global variable


NEW QUESTION # 246
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