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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You're working with Snowpark and have a DataFrame 'df containing a column 'json_data' with JSON strings. Some of these JSON strings are invalid. You need to parse the valid JSON strings and extract a field named 'product_id' from them. Invalid JSON strings should result in a 'NULL' value for the extracted 'product_id'. Which of the following approaches is the MOST robust and efficient way to achieve this?

A)

B)

C)

D)

E)

2. You have developed a Python function that performs complex data transformation on customer data'. You want to operationalize this function as a UDTF in Snowpark to process large datasets efficiently. The function takes a customer ID and a list of transaction amounts as input and returns a table with calculated risk scores for each transaction. Which of the following code snippets correctly defines and registers this UDTF in Snowpark, ensuring proper type handling and scalability?

A)

B)

C) Options B and C are the most correct.
D)

E)

3. You are developing a Snowpark application to load data into a Snowflake table named 'SALES DATA. The DataFrame 'sales_df contains new sales records. You need to insert these records into 'SALES DATA. Which of the following Snowpark DataFrame methods will efficiently perform this operation, considering potential data type mismatches between the DataFrame and the target table? Assume no explicit schema definition is necessary.

A) Option C
B) Option A
C) Option B
D) Option D
E) Option E

4. You are developing a Snowpark stored procedure in Python that utilizes the 'requests' library to fetch data from an external API. Your Snowflake account is configured to use Anaconda packages. You encounter an error indicating that the 'requests' library is not found. Which of the following steps are MOST effective in ensuring the 'requests' library is available to your stored procedure?

A) Include the 'requests' library directly in the stored procedure code using a base64 encoded string.
B) Install the 'requestS library directly onto the Snowflake compute nodes using SnowSQL's command.
C) Specify the 'requests library in the stored procedure's 'packages argument during creation: 'CREATE OR REPLACE PROCEDURE
D) Manually upload the 'requests' library's ' .py' files to an internal stage and import them within the stored procedure.
E) Enable Anaconda integration for your Snowflake account, ensuring 'requests' is available in the Snowflake Anaconda channel, and then create the stored procedure using 'imports=['snowflake://packages/requests/']'.

5. You have a large CSV file containing product descriptions that you need to analyze using a sentiment analysis UDF. The CSV file is too large to fit in memory on your local machine. You want to stream the data directly from a Snowflake stage to your UDF for processing, avoiding the need to download the entire file. Which of the following approaches allows you to achieve this using Snowpark?

A) Create an external table pointing to the CSV file on the stage. Use a Snowpark DataFrame to select the product descriptions from the external table and then apply the sentiment analysis UDF.
B) Load the CSV file into a Snowflake internal stage. Then, within a Snowpark UDF, directly query the stage using SQL to retrieve and analyze the product descriptions.
C) Use the 'snowflake.connector' library within the UDF to establish a new connection to Snowflake, read the CSV file from the stage, and analyze the product descriptions.
D) Read the CSV file directly into a Pandas DataFrame using within the UDF and perform sentiment analysis on the DataFrame.
E) Use the method within the UDF to read the CSV file line by line and pass each product description to the sentiment analysis function.

Solutions: