Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are building a predictive model for customer churn using linear regression in Snowflake. You have identified several features, including 'CUSTOMER AGE', 'MONTHLY SPEND', and 'NUM CALLS'. After performing an initial linear regression, you suspect that the relationship between 'CUSTOMER AGE and churn is not linear and that older customers might churn at a different rate than younger customers. You want to introduce a polynomial feature of "CUSTOMER AGE (specifically, 'CUSTOMER AGE SQUARED') to your regression model within Snowflake SQL before further analysis with python and Snowpark. How can you BEST create this new feature in a robust and maintainable way directly within Snowflake?

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

2. You are working with a dataset containing timestamps representing website user activity. The timestamps are stored as strings in the format 'YYYY-MM-DD HH:MI:SS.SSSSSS' in a Snowflake table named 'website_activity'. You need to extract the hour of the day from these timestamps and encode it as a cyclical feature using sine and cosine transformations. This is to capture the cyclical nature of user activity throughout the day (e.g., 23:00 and 00:00 are close in time). Which of the following Snowflake SQL code snippets correctly implements this cyclical encoding and creates the 'hour_sin' and 'hour_cos' columns?

A)

B)

C)

D)

E)

3. A data scientist is tasked with building a real-time customer support system using Snowflake Cortex. The system needs to analyze incoming customer messages and categorize them into predefined issue types (e.g., billing, technical support, account management) for efficient routing to the appropriate support team. Considering the need for low latency and high accuracy, which of the following approaches would be the MOST suitable for implementing this categorization task using Snowflake Cortex, considering the costs and trade-offs involved?

A) Developing a custom Python UDF that uses a third-party LLM API (e.g., OpenAl) to categorize the messages and deploying it in Snowflake, handling API authentication and rate limiting within the UDF.
B) Leveraging the Snowflake Cortex built-in categorization task-specific model (e.g., using the 'SNOWFLAKE.ML.PREDICT' function with the appropriate model name) to categorize incoming messages without any fine-tuning.
C) Creating a series of SQL 'CASE' statements to categorize the messages based on keyword matching within the message text. Use regular expressions for more complex pattern matching.
D) Directly calling the Snowflake Cortex 'COMPLETE' endpoint with a detailed prompt for each incoming message, instructing it to categorize the message based on the predefined issue types.
E) Fine-tuning a pre-trained language model within Snowflake using the 'CREATE SNOWFLAKE.ML.ANACONDA_MODEL' command on a dataset of historical customer messages and their corresponding issue types, then deploying this fine-tuned model for real-time categorization via a user-defined function (UDF).

4. A data science team is evaluating different methods for summarizing lengthy customer support tickets using Snowflake Cortex. The goal is to generate concise summaries that capture the key issues and resolutions. Which of the following approaches is/are appropriate for achieving this goal within Snowflake, considering the need for efficiency, cost-effectiveness, and scalability? (Select all that apply)

A) Creating a custom summarization model using a transformer-based architecture like BART or T5, training it on a large dataset of support tickets and summaries within Snowflake using Snowpark ML, and then deploying this custom model for generating summaries via a UDF.
B) Developing a Python UDF that leverages a pre-trained summarization model from a library like 'transformers' and deploying it in Snowflake. Managing the model loading and inference within the UDF.
C) Using the 'SNOWFLAKE.ML.PREDICT' function with a summarization task-specific model provided by Snowflake Cortex, passing the full ticket text as input to generate a summary.
D) Calling the Snowflake Cortex 'COMPLETE' endpoint with a detailed prompt that instructs the model to summarize the support ticket, explicitly specifying the desired summary length and format.
E) Employing a SQL-based approach using string manipulation functions and keyword extraction techniques to identify important sentences and concatenate them to form a summary.

5. You are developing a Python UDTF in Snowflake to perform time series forecasting. You need to incorporate data from an external REST API as part of your feature engineering process within the UDTF. However, you are encountering intermittent network connectivity issues that cause the UDTF to fail. You want to implement a robust error handling mechanism to gracefully handle these network errors and ensure that the UDTF continues to function, albeit with potentially less accurate forecasts when external data is unavailable. Which of the following approaches is the MOST appropriate and effective for handling these network errors within your Python UDTF?

A) Use the 'try...except' block specifically around the code that makes the API call. Within the 'except block, catch specific network-related exceptions (e.g., requests.exceptions.RequestException', 'socket.timeout'). Log the error to a Snowflake stage using the 'logging' module and retry the API call a limited number of times with exponential backoff.
B) Before making the API call, check the network connectivity using the 'ping' command. If the ping fails, skip the API call and return a default forecast value. This prevents the UDTF from attempting to connect to an unavailable endpoint.
C) Use a combination of retry mechanisms (like the tenacity library) with exponential backoff around the API call. If the retry fails after a predefined number of attempts, then return pre-computed data or use a simplified model as the UDTF's output.
D) Configure Snowflake's network policies to allow outbound network access from the UDTF to the specific REST API endpoint. This will eliminate the network connectivity issues and prevent the UDTF from failing.
E) Implement a global exception handler within the UDTF that catches all exceptions, logs the error message to a Snowflake table, and returns a default forecast value when a network error occurs. Ensure the error logging table exists and has sufficient write permissions for the UDTF.

Solutions: