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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You have deployed a custom model using Snowpark within Snowflake. The model is designed to predict customer churn, and you've wrapped it in a User-Defined Function (UDF) for easy use. The UDF takes several customer features as input and returns a churn probability. However, you notice the UDF's performance is slow, especially when scoring large batches of customers. Which of the following strategies would be most effective in optimizing the performance of your model deployment within Snowflake? Assume the UDF is already using vectorization techniques.

A) Re-write the UDF in SQL instead of Snowpark to avoid the overhead of the Snowpark API.
B) Utilize a vectorized UDF that can process multiple rows in a single call, further leveraging Snowflake's parallel processing capabilities. Ensure it supports the correct data types for both input and output. Consider using a Pandas UDF if Python is the underlying language.
C) Implement row-level security on the input data. This enhances security and implicitly improves query performance because the model only processes authorized data.
D) Cache the results of the UDF using Snowflake's result caching feature. This will avoid re-executing the UDF for the same input values.
E) Increase the warehouse size used by Snowflake. This provides more resources for the UDF execution.

2. You are tasked with deploying a pre-trained sentiment analysis model hosted externally using AWS SageMaker. The model endpoint requires an API key for authentication, and you want to score customer reviews stored in a Snowflake table named 'CUSTOMER REVIEWS. Which of the following steps are necessary to securely and efficiently integrate this external model with Snowflake, assuming you have already created a Snowflake stage to store secrets?

A) Create a secret object in Snowflake to store the API key. Grant appropriate privileges on the secret to the role that will execute the external function. Modify external function that references secure external stage.
B) Create an external function in Snowflake that invokes the SageMaker endpoint, hardcoding the API key directly into the function definition for simplicity.
C) Create an external function in Snowflake that retrieves the API key from a secure Snowflake secret object. Grant USAGE privilege on the secret to the service account associated with the external function.
D) Use Snowflake's external functions to directly call the SageMaker endpoint from a SQL query, passing the customer review text as input. No separate secure external stage configuration is needed as long as Snowflake has internet access.
E) Store the API key in an environment variable within the AWS Lambda function (if using API Gateway) that serves as an intermediary between Snowflake and SageMaker. Snowflake calls the API Gateway endpoint which relays the request to the SageMaker endpoint, and no specific configuration is needed on snowflake.

3. You are tasked with building a data science pipeline in Snowflake to predict customer churn. You have trained a scikit-learn model and want to deploy it using a Python UDTF for real-time predictions. The model expects a specific feature vector format. You've defined a UDTF named 'PREDICT CHURN' that loads the model and makes predictions. However, when you call the UDTF with data from a table, you encounter inconsistent prediction results across different rows, even when the input features seem identical. Which of the following are the most likely reasons for this behavior and how would you address them?

A) The UDTF is not partitioning data correctly. Ensure the UDTF utilizes the 'PARTITION BY clause in your SQL query based on a relevant dimension (e.g., 'customer_id') to prevent state inconsistencies across partitions. This will isolate the impact of any statefulness within the function
B) The issue is related to the immutability of the Snowflake execution environment for UDTFs. To resolve this, cache the loaded model instance within the UDTF's constructor and reuse it for subsequent predictions. Using a global variable is also acceptable.
C) The input feature data types in the table do not match the expected data types by the scikit-learn model. Cast the input columns to the correct data types (e.g., FLOAT, INT) before passing them to the UDTF. Use explicit casting functions like 'TO DOUBLE and INTEGER in your SQL query.
D) There may be an error in model, where the 'predict method is producing different ouputs for the same inputs. Retraining the model will resolve the issue.
E) The scikit-learn model was not properly serialized and deserialized within the UDTF. Ensure the model is saved using 'joblib' or 'pickle' with appropriate settings for cross-platform compatibility and loaded correctly within the UDTF's 'process' method. Verify serialization/deserialization by testing it independently from Snowflake first.

4. You are performing exploratory data analysis on a dataset containing customer transaction data in Snowflake. The dataset has a column named 'transaction_amount' and a column named 'customer_segment'. You want to analyze the distribution of transaction amounts for each customer segment using Snowflake's statistical functions. Which of the following approaches would BEST achieve this, providing insights into the central tendency and spread of the data?

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

5. You are using Snowflake ML to predict housing prices. You've created a Gradient Boosting Regressor model and want to understand how the 'location' feature (which is categorical, representing different neighborhoods) influences predictions. You generate a Partial Dependence Plot (PDP) for 'location'. The PDP shows significantly different predicted prices for each neighborhood. Which of the following actions would be MOST appropriate to further investigate and improve the model's interpretability and performance?

A) Generate ICE (Individual Conditional Expectation) plots alongside the PDP to assess the heterogeneity of the relationship between 'location' and predicted price.
B) Combine the PDP for 'location' with a two-way PDP showing the interaction between 'location' and 'square_footage'.
C) Use one-hot encoding for the 'location' feature and generate individual PDPs for each one-hot encoded column.
D) Replace the 'location' feature with a numerical feature representing the average house price in each neighborhood, calculated from historical data.
E) Remove the 'location' feature from the model, as categorical features are inherently difficult to interpret.

Solutions: