Snowflake SPS-C01 Exam Duration | New SPS-C01 Braindumps Files

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q25-Q30):

NEW QUESTION # 25
You are building a Snowpark application that processes a large number of PDF files stored in a Snowflake stage. You need to extract text from each PDF file using a Python UDF and store the extracted text in a Snowflake table. You are considering different approaches for loading the PDF files into the UDE Which of the following approaches would provide the BEST performance and scalability, while minimizing network traffic and memory usage?

Answer: B

Explanation:
Option C is the most efficient approach. 'snowflake.snowpark.files.SnowflakeFile' allows the UDF to directly access the PDF files stored in the Snowflake stage without transferring the entire file to the client. This minimizes network traffic and memory usage. Option A requires loading all PDF files into a pandas DataFrame, which can consume a significant amount of memory. Option B has issues relating to the file size and content restrictions and isn't suitable for many files. Option D involves downloading all files to a local directory, which is not scalable and introduces unnecessary overhead. Option E using 'GET OBJECT is outside the scope of the python api.


NEW QUESTION # 26
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 # 27
You have a Snowpark application that utilizes a vectorized Python UDF to perform complex calculations on a large dataset. You notice that the performance is still not optimal. You suspect that the bottleneck might be related to how the data is being partitioned and processed by Snowflake. Which of the following actions, when performed in conjunction with vectorization, would MOST likely improve performance?

Answer: E

Explanation:
Repartitioning the DataFrame using allows you to control how the data is distributed across compute nodes. This can improve performance by ensuring that related data is processed together, reducing data shuffling and improving data locality. Pre- sorting data (A) might help in some cases, but it doesn't guarantee optimal data distribution for parallel processing. Broadcasting the DataFrame (C) is suitable for smaller datasets, not large ones where it can lead to memory issues. Converting the DataFrame to a Pandas DataFrame (D) defeats the purpose of using Snowpark for distributed processing and introduces a single-node bottleneck. There's no direct control over the number of UDF worker threads in Snowflake.


NEW QUESTION # 28
Consider a DataFrame 'products df loaded from a SnoMlake table. It contains a 'features' column of type VARIANT, where each row contains a JSON object representing product features. Your task is to create a new DataFrame where each feature becomes a separate column. You need to dynamically extract these features without knowing the specific feature names in advance. Which of the following approaches could achieve this using Snowpark, and what considerations are important? Choose all that apply:

Answer: B,C

Explanation:
Options B and C are viable approaches. Option B: You can use the native function on the VARIANT column to extract the keys, then iterate over the returned array to dynamically create new columns. This relies on knowing the structure of the data at runtime, but doesn't require a UDE Option C: FLATTEN' offers a SQL-centric way to achieve this, which might be preferable for performance and maintainability. After flattening, you would typically pivot the data. Option A is possible with IJDFs, but might be less performant than using native functions or FLATTEN. Option D is incorrect; dynamic column creation is possible. While OBJECT_CONSTRUCT() can construct JSON objects, it's not directly helpful for dynamically extracting JSON properties into separate columns in this scenario (Option E).


NEW QUESTION # 29
You have a Snowpark application processing streaming data from an event table. You observe that the application frequently fails with transient errors related to network connectivity or Snowflake service unavailability. You want to implement a robust error handling strategy to ensure the application can recover from these transient failures without losing data'. Which of the following approaches would be MOST appropriate and effective in this scenario, ensuring idempotent processing?

Answer: B,C

Explanation:
Implementing a message queue provides a buffer that isolates the Snowpark application from transient data source failures. E is correct because adding an exponential backoff mechanism with jitter is crucial to prevent overwhelming the system with retries and helps to ensure idempotent processing. Option B can address some internal Snowflake errors, but not connectivity issues. The other approaches do not address data loss or idempotent operation.


NEW QUESTION # 30
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