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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a DataFrame 'df in Snowpark representing customer data'. One of the columns, 'customer_details', contains JSON objects with varying structures. Some objects contain 'address' and 'phone' fields, while others only contain 'email'. You need to write a Snowpark query to extract the 'city' from the 'address' field if it exists; otherwise, return NULL. What is the most efficient way to achieve this using the function?

A)

B)

C)

D)

E)

2. You've created a Snowpark Python stored procedure designed to perform sentiment analysis on customer reviews stored in a Snowflake table. This procedure utilizes a third-party Python library, 'transformers', for its sentiment analysis model. You need to operationalize this stored procedure for scheduled execution. Which of the following options represents the MOST efficient and reliable approach for deploying and managing the 'transformers' dependency within the Snowflake environment for your stored procedure, minimizing deployment complexity and potential runtime errors?

A) Install the 'transformers' library on the Snowflake compute warehouse nodes directly using a startup script.
B) Create a Snowflake Anaconda channel, upload the 'transformers' library to this channel, and specify the channel in the stored procedure definition using the packageS parameter.
C) Include the 'transformers' library as a part of the task definition and make it available for the stored procedure that the task is invoking.
D) Include the 'transformers' library directly within the stored procedure's Python code as a string literal and execute it using or 'eval()'.
E) Upload the 'transformers' library as a zip file to a Snowflake stage and reference it in the 'imports' parameter when creating the stored procedure.

3. Consider a Snowflake table 'sales_data' with a VARIANT column 'order_details' containing an array of JSON objects, where each object represents an item in an order. Each item object has fields like 'quantity', and 'price'. You need to calculate the total price for each order by summing the product of 'quantity' and 'price' for all items in the 'order_details' array. Which of the following Snowpark Python snippets correctly accomplishes this?

A)

B)

C)

D)

E)

4. You are using Snowpark in Python within a Jupyter Notebook environment to analyze sales data'. You've established a connection to Snowflake and loaded your data into a Snowpark DataFrame named 'sales ff. You need to calculate the cumulative sales for each product category over time. The 'sales_df' DataFrame has columns 'SALE DATE' (DATE), 'PRODUCT CATEGORY' (VARCHAR), and 'SALE AMOUNT' (NUMBER). Which of the following approaches, or combination of approaches, will correctly calculate the cumulative sales while optimizing for Snowflake's performance and scalability? (Select all that apply)

A) Use session.sql() to execute a SQL query with a window function for cumulative sales and load the data into snowpark DataFrame.
B) Use the and 'sort()' functions on the Snowpark DataFrame to order data by 'PRODUCT CATEGORY' and 'SALE DATE before calculating the cumulative sum using a UDF.
C) Iterate through the rows of the 'sales_df DataFrame in the Jupyter Notebook and manually calculate the cumulative sales using Python code.
D) Write a stored procedure in Snowflake that calculates the cumulative sales and call the stored procedure from your Snowpark application.
E) Use a Window function within a Snowpark DataFrame transformation to calculate the cumulative sum for each product category, ordered by 'SALE_DATE.

5. You are working with a Snowpark DataFrame containing product review data'. The DataFrame has a 'review_text' column containing unstructured text reviews. Your task is to perform sentiment analysis on these reviews using Snowpark for Python. However, you are restricted to using only Snowpark built-in functions and UDFs; you cannot use external libraries like NLTK or TextBlob directly within your Snowpark code. Given this contraint, what is a valid approach to enrich your dataframe?

A) Use Snowpark's 'transform' function with a custom Python transformer that leverages an internal vocabulary of positive and negative words to assign a sentiment score to each review based on word frequency. Then store the result in the column.
B) Build a Snowpark Python UDF that uses regular expressions to identify keywords and phrases indicative of positive, negative, or neutral sentiment. Assign a score based on the presence and frequency of these keywords. You can then apply 'when' statements to get .
C) Create a Java UDF within Snowflake that utilizes a Java-based sentiment analysis library (e.g., Stanford CoreNLP) to process the 'review_text' and return the sentiment score. Then, call this Java UDF from your Snowpark Python code.
D) Leverage Snowflake's external function capabilities to call a pre-trained sentiment analysis model hosted on a cloud ML platform (e.g., AWS SageMaker, Azure Machine Learning), passing the 'review_text' as input and receiving the sentiment score as output.
E) Develop a sentiment analysis microservice deployed outside Snowflake, then use Snowpark's 'call_udf function to invoke this service, passing the 'review_text' as input and receiving the sentiment score as output.

Solutions: