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How Can You Successfully Get the Quality Snowflake SPS-C01 Exam Questions?

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How Can You Successfully Get the Quality Snowflake SPS-C01 Exam Questions?

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q123-Q128):NEW QUESTION # 123
You are developing a Snowpark stored procedure in Python to perform sentiment analysis on customer reviews. The procedure relies on a custom Python library, 'sentiment_analyzer.py' , which is not available in Snowflake's default Anaconda channel. You also need to include the 'nltk' library. Which of the following approaches is the MOST efficient and recommended way to make both dependencies available to your stored procedure within Snowflake?
  • A. Create a Snowflake Anaconda channel package containing 'sentiment_analyzer.pV and 'nltk' using 'conda build' , then reference this package in your stored procedure's 'imports' parameter.
  • B. Install 'sentiment_analyzer.py' and 'nltk' on each Snowflake virtual warehouse node and set the 'PYTHONPATH' environment variable. (This will require contacting Snowflake support.)
  • C. Upload 'sentiment_analyzer.py' and 'nltk"s compiled code as separate stages, then import them within the stored procedure using 'sys.path.append()'.
  • D. Include the code from 'sentiment_analyzer.py' directly within the stored procedure's Python code and download 'nltk' modules from the internet each time the stored procedure is executed.
  • E. Create a ZIP file containing 'sentiment_analyzer.py' and the required 'nltk' modules, upload it to a stage, and specify the stage path in the 'imports' parameter of the 'sproc' decorator.
Answer: E
Explanation:
Option C is the most efficient and recommended approach. Snowflake allows importing dependencies from a stage as a ZIP file. This avoids the complexity of creating a custom Anaconda package (Option B) or manually managing dependencies on each virtual warehouse node (Option D), which is not supported. Directly including the code (Option E) makes the procedure large and difficult to manage. Using (Option A) is generally discouraged as it's less robust for dependency management in Snowpark stored procedures.

NEW QUESTION # 124
A data engineering team is using Snowpark Python to build a complex ETL pipeline. They notice that certain transformations are not being executed despite being defined in the code. Which of the following are potential reasons why transformations in Snowpark might not be executed immediately, reflecting the principle of lazy evaluation? Select TWO correct answers.
  • A. Snowpark operations are only executed when an action (e.g., 'collect()', 'show()', is called on the DataFrame or when the DataFrame is materialized.
  • B. Snowpark employs lazy evaluation to optimize query execution by delaying the execution of transformations until the results are actually required.
  • C. The 'eager_execution' session parameter is set to 'True'.
  • D. Snowpark automatically executes all transformations as soon as they are defined, regardless of whether the results are needed.
  • E. The size of the data being processed exceeds Snowflake's memory limits, causing transformations to be skipped.
Answer: A,B
Explanation:
Snowpark employs lazy evaluation, which means transformations are not executed until an action is performed on the DataFrame. This allows Snowflake to optimize the entire query plan before execution. Setting 'eager_execution' to True does NOT exist in Snowpark Python. Data size exceeding Snowflake's limits would result in an error, not skipped transformations.

NEW QUESTION # 125
You have a Snowpark Python application that uses a UDF to perform custom data transformations. The UDF relies on a large, read-only lookup table that is stored as a CSV file on a Snowflake stage. Which of the following strategies would be the MOST efficient way to access the lookup table within the UDF?
  • A. Load the CSV file into a Snowflake stage, and in the python UDF code, use the get_stage_file API from session object to read the file once. Then the data cached in-memory within the UDF module, and reuse the cached data for subsequent calls.
  • B. Load the CSV file into a Snowflake table and then query the table from within the UDF using 'session.sql(V.
  • C. Use the 'cachetoolS library with a Least Recently Used (LRU) cache to store the lookup table in memory. The UDF will check the cache before reading the CSV file, and update the cache if necessary. The CSV file is read with get_stage_file API from session.
  • D. Read the CSV file from the stage once when the UDF is first called, cache the data in a global variable within the UDF module, and then reuse the cached data for subsequent calls.
  • E. Read the CSV file from the stage every time the UDF is called using 'snowflake.connector.connect()' and then load the data into a Pandas DataFrame within the UDF function.
Answer: B,C
Explanation:
Both options C and E are efficient. Option C leverages Snowflake's internal storage and query capabilities, avoiding repeated file reads. Although reading a file from stage only at once is good, it would impact the first call. Option E avoids hitting the stage every time, and only when the key is not present in cache. Options A and B suffer from performance bottlenecks due to repeated file access. In general, reading data within the Snowflake environment (C) is more performant than reading data from external sources within the UDF, especially when Snowflake's query optimizer can be used. Caching with LRU using the 'cachetoolS library is effective, as cache would contain some values from large data. get_stage_file API from session object is efficient way.

NEW QUESTION # 126
A data engineer wants to create a Snowpark session using environment variables defined in a .env' file. The file contains the following: SNOWFLAKE ACCOUNT=myaccount.snowflakecomputing.com SNOWFLAKE USER=snowpark_user SNOWFLAKE SNOWFLAKE DATABASE=mydb SNOWFLAKE SCHEMA=myschema SNOWFLAKE WAREHOUSE=mywarehouse Which code snippet correctly establishes a Snowpark session using these environment variables?
  • A.
  • B.
  • C.
  • D.
  • E.
Answer: A
Explanation:
Option E is the most concise and recommended way to create a Snowpark session using environment variables defined in a .env file, by using method. Option B works but requires manual loading of environment variables. Options A and C do not correctly access environment variables. Option D would require the environment variables to be named exactly as the session builder expects and doesn't use the dotenv library which is designed for this purpose.

NEW QUESTION # 127
You are developing a Snowpark Python application to process large datasets stored in a Snowflake table called 'CUSTOMER DATA' The application needs to perform complex data transformations and aggregations that benefit from Snowpark's lazy evaluation and query optimization. Which of the following approaches will lead to the MOST efficient execution in terms of resource utilization and performance?
  • A. Create a Snowpark DataFrame from the "CUSTOMER_DATX table using 'session.table('CUSTOMER DATA')', perform all the transformations and aggregations using Snowpark DataFrame API, and materialize the final result into a new Snowflake table using .
  • B. Load the data into a temporary table, then create a Stored Procedure in SQL to perform transformations, and call the procedure from the Snowpark Python application.
  • C. Execute a series of individual SQL queries using 'session.sql()' to perform the transformations and aggregations, and then combine the results into a final Pandas DataFrame for further processing before loading it back into Snowflake.
  • D. Fetch all the data from 'CUSTOMER_DATR into a Pandas DataFrame using , perform the transformations in Pandas, and then load the results back into a new Snowflake table using 'session.write_pandas()'.
  • E. Use 'session.table('CUSTOMER to fetch all the data into a list of Row objects in Python, perform transformations using standard Python loops and data structures, and then write the processed data to a new Snowflake table using 'session.createDataFrame(V.
Answer: A
Explanation:
Option B is the most efficient. Snowpark's lazy evaluation and query optimization allow Snowflake to execute the entire transformation pipeline within the data warehouse, minimizing data transfer and leveraging Snowflake's compute resources. Fetching all data to Pandas (A and D) moves computation out of Snowflake. Executing individual SQL queries (C) loses Snowpark's optimization benefits. SQL stored procedure (E) might be more efficient than some options, but Snowpark provides a better integrated solution.

NEW QUESTION # 128
......
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