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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are working with a Snowpark DataFrame called 'customer df that contains customer data, including a column named 'registration_date' of data type TIMESTAMP NTZ. You need to filter the DataFrame to only include customers who registered in the year 2023. Which of the following Snowpark code snippets represents the MOST efficient and correct way to accomplish this filtering, considering potential timezone issues?

A)

B)

C)

D)

E)

2. A Snowpark application needs to authenticate to Snowflake using OAuth. The application is running on an Azure Function and uses a client ID, client secret, and refresh token obtained previously. Which of the following connection parameter dictionaries is correctly configured for OAuth authentication?

A)

B)

C)

D)

E)

3. You have a Snowpark DataFrame named and want to create a stored procedure that calculates the average purchase amount for each customer. The stored procedure should accept the DataFrame as input, perform the aggregation, and return a new DataFrame with the results. Which of the following code snippets BEST demonstrates how to correctly define and deploy this stored procedure?

A)

B)

C)

D)

E)

4. 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) Install 'sentiment_analyzer.py' and 'nltk' on each Snowflake virtual warehouse node and set the 'PYTHONPATH' environment variable. (This will require contacting Snowflake support.)
B) 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.
C) 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.
D) 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.
E) Upload 'sentiment_analyzer.py' and 'nltk"s compiled code as separate stages, then import them within the stored procedure using 'sys.path.append()'.

5. You are using Snowpark Python to process a large dataset. You need to persist a DataFrame to a Snowflake table but want to ensure the operation is as efficient as possible and minimizes the data transfer overhead. The table already exists with the appropriate schema. Which of the following strategies would be the MOST efficient way to write the DataFrame to the existing table?

A) Use 'df.write.insertlnto('existing_table')'.
B) Use with the default save mode (which is 'append').
C) Create a temporary table, load the DataFrame into it using , and then use a 'CREATE OR REPLACE TABLE AS SELECT FROM temp_table' statement.
D) Write the DataFrame to a stage location as Parquet files and then use a 'COPY INTO existing_table FROM command.
E) Use 'df.write.mode('overwrite').saveAsTable('existing_table'Y.

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