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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. 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) 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.
B) 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.
C) 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 .
D) 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.
E) 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.

2. You have a Snowpark Python application that reads data from a Snowflake table, performs several transformations, and then writes the results back to a new Snowflake table. The transformations involve complex calculations and aggregations. During testing, you observe that the application is consuming a significant amount of credits. Which of the following optimization strategies would be MOST effective in reducing the credit consumption of your Snowpark application?

A) Minimize the amount of data transferred between Snowpark and Snowflake by pushing down transformations and using stored procedures where appropriate.
B) Convert all Python User-Defined Functions (UDFs) to Java User-Defined Table Functions (UDTFs) for improved performance.
C) Use the 'cache()' method on intermediate Snowpark DataFrames to avoid recomputation of transformations.
D) Disable auto-scaling on the Snowpark-optimized warehouse to prevent it from scaling up unnecessarily.
E) Optimize the SQL queries generated by Snowpark by explicitly specifying join hints and using appropriate indexes.

3. You are developing a Snowpark application to analyze customer data'. You need to create a Snowpark DataFrame from a list of dictionaries, where each dictionary represents a customer with 'id', 'name', and 'city' keys. The data should be loaded efficiently. Consider these scenarios: 1 . The input data can sometimes contain missing values (e.g., a customer might not have a city specified). 2. You want to ensure optimal performance when loading the data, as the list can be very large. 3. You need the resulting DataFrame's schema to correctly infer the datatypes based on the input dictionary's values. Which of the following methods and considerations should be used to create a Snowpark DataFrame from a list of dictionaries to meet these requirements?

A) Use 'session.createDataFrame(data, where 'schema' is explicitly defined to handle missing data and ensure correct data types. This improves performance over schema inference.
B) Use 'session.createDataFrame(data, and specify the 'nullable' property for each field within the schema. Additionally, define the data type explicitly. This optimizes performance.
C) Leverage Snowpark's optimized data loading by converting the list of dictionaries to a Pandas DataFrame first and then create a Snowpark DataFrame using 'session.createDataFrame(pandas_df)'. Pandas has optimized data loading.
D) Use 'session.createDataFrame(data)' with default settings. Snowflake will automatically infer the schema and handle missing values as 'NULL'.
E) Use 'session.createDataFrame(data)' and then explicitly cast columns with potential missing values to the correct datatype using method to ensure 'NULL' handling.

4. You are tasked with building a Snowpark Python application to process JSON files stored in a Snowflake stage. The JSON files contain customer feedback data, including sentiment scores. You need to create a stored procedure that reads the JSON files, calculates the average sentiment score, and stores the result in a Snowflake table. You also need to handle potential errors, such as invalid JSON format in some files, and continue processing other files. Which of the following approaches is MOST efficient and robust to handle this scenario?

A) Use the function directly within the stored procedure to parse each JSON file. Catch exceptions within the loop and log errors to a separate table. Use 'DataFrame.write.mode('append')' to write the average sentiment score to the target table.
B) Load the JSON data into a VARIANT column in a temporary table. Use a Snowpark DataFrame transformation to parse the JSON data from the VARIANT column. Catch errors during the DataFrame transformation process and log them to a separate table. Finally, calculate the average sentiment score using Snowpark functions.
C) Download the JSON files to the Snowpark client, process them using standard Python libraries (like 'json'), and then upload the aggregated data back to Snowflake using 'session.write_pandas()'. Handle errors locally and log them.
D) Implement an external function using AWS Lambda or Azure Functions to parse the JSON files and calculate the average sentiment score. Call this external function from the stored procedure. This provides better error handling and scalability.
E) Use and return a DataFrame containing the average sentiment score and file name for each processed file. Handle JSON parsing errors by skipping the file and logging the error. Use to write the DataFrame to the target table.

5. You have a Snowpark DataFrame named 'orders_df with columns 'order_id', 'customer_id', 'order_date', and 'order_total'. You need to perform the following data enrichment steps using Snowpark for Python: 1. Calculate the 'year' from the 'order_date' column. 2. Calculate the 'discounted_total' by applying a discount of 10% if the 'order_total' is greater than $100, otherwise, no discount. 3. Create a new column 'customer_tier' based on the total spend per customer for each year. Customers with total spend greater than $1000 are 'Gold', between $500 and $1000 are 'Silver', and below $500 are 'Bronze'. Which of the following code snippets correctly implements these data enrichment steps using Snowpark (Assume the existence of a customer total spend df DataFrame).

A)

B)

C)

D)

E)

Solutions: