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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You're working on a fraud detection system for an e-commerce platform. You have a table 'TRANSACTIONS with a 'TRANSACTION AMOUNT column. You want to bin the transaction amounts into several risk categories ('Low', 'Medium', 'High', 'Very High') using explicit boundaries. You want the bins to be inclusive of the lower boundary and exclusive of the upper boundary (e.g., [0, 100), [100, 500), etc.). Which of the following SQL statements using the 'WIDTH BUCKET function correctly bins the transaction amounts into these categories, assuming these boundaries: 0, 100, 500, 1000, and infinity, and assigns appropriate labels?

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

2. You are developing a machine learning model within a Snowflake UDF (User-Defined Function) written in Python. This UDF needs to access external Python libraries not included in the default Snowflake Anaconda channel. You've created a stage and uploaded the necessary file. You've successfully used 'conda create' and 'conda install --file requirements.txt' to create your environment locally, and subsequently zipped the environment. Now, what steps are essential to configure the Snowflake UDF to correctly use these external libraries from the stage? Select all that apply.

A) Install the packages directly into the Snowflake environment using 'CREATE OR REPLACE FUNCTION RETURNS VARCHAR ..: and a pip install command within the function.
B) Specify the stage path containing the zipped environment in the 'imports' clause of the 'CREATE OR REPLACE FUNCTION' statement using the symbol and specifying the zip file e.g., '@snowflake_packages/myenv.zip'.
C) Create a ZIP file containing the Python environment and upload it to a Snowflake stage.
D) Include the line 'import sys; sys._xoptions['snowflake_home'] = at the top of your UDF to point to the environment stage location.
E) Set the 'PYTHON_VERSION' parameter of the 'CREATE OR REPLACE FUNCTION' statement to match the Python version used in your environment using e.g. 'PYTHON_VERSION = '3.8".

3. A data scientist is analyzing website click-through rates (CTR) for two different ad campaigns. Campaign A ran for two weeks and had 10,000 impressions with 500 clicks. Campaign B also ran for two weeks with 12,000 impressions and 660 clicks. The data scientist wants to determine if there's a statistically significant difference in CTR between the two campaigns. Assume the population standard deviation is unknown and unequal for the two campaigns. Which statistical test is most appropriate to use, and what Snowflake SQL code would be used to approximate the p-value for this test (assume 'clicks_b' , and are already defined Snowflake variables)?

A) An independent samples t-test, because we are comparing the means of two independent samples. Snowflake code: SELECT

B) A one-sample t-test, because we are comparing the sample mean of campaign A to the sample mean of campaign Snowflake code: 'SELECT t_test_lsamp(clicks_a/impressions_a - clicks_b/impressions_b, 0)'
C) An independent samples t-test (Welch's t-test), because we are comparing the means of two independent samples with unequal variances. Snowflake code (approximation using UDF - assuming UDF 'p_value_from_t_stat' exists that calculates p-value from t-statistic and degrees of freedom):

D) Az-test, because we know the population standard deviation. Snowflake code: 'SELECT normcdf(clicks_a/impressions_a - clicks_b/impressions_b, O, 1)'
E) A paired t-test, because we are comparing two related samples over time. Snowflake code: 'SELECT t_test_ind(clicks_a/impressions_a, 'VAR EQUAL-TRUE')

4. You are deploying a large language model (LLM) to Snowflake using a user-defined function (UDF). The LLM's model file, '11m model.pt', is quite large (5GB). You've staged the file to Which of the following strategies should you employ to ensure successful deployment and efficient inference within Snowflake? Select all that apply.

A) Split the large model file into smaller chunks and stage each chunk separately. Reassemble the model within the UDF code before inference.
B) Leverage Snowflake's Snowpark Container Services to deploy the LLM as a separate containerized application and expose it via a Snowpark API. Then call that endpoint from snowflake.
C) Increase the warehouse size to XLARGE or larger to provide sufficient memory for loading the large model into the UDF environment.
D) Use the 'PUT' command with to compress the model file before staging it. Snowflake will automatically decompress it during UDF execution.
E) Use the 'IMPORTS' clause in the UDF definition to reference Ensure the UDF code loads the model lazily (i.e., only when it's first needed) to minimize startup time and memory usage.

5. You are troubleshooting an external function in Snowflake that calls a model hosted on Google Cloud A1 Platform. The external function consistently returns 'SQL compilation error: External function error: HTTP 400 Bad Request'. You have verified the API integration is correctly configured, and the Google Cloud project has the necessary permissions. Which of the following is the most likely cause of this error, and how would you best diagnose it?

A) The API integration in Snowflake is missing the necessary authentication credentials for Google Cloud. Diagnose by re-creating the API integration and ensuring the correct service account and scopes are configured.
B) There is a mismatch between the request headers sent by Snowflake and what the Google Cloud AI Platform endpoint expects, specifically the 'Content-Type'. Diagnose by examining the headers being sent by Snowflake and ensuring they match the expected format.
C) The request payload being sent by Snowflake exceeds the maximum size limit allowed by Google Cloud AI Platform. Diagnose by reducing the size of the input data and testing again.
D) The issue is most likely due to incorrect data types being passed from Snowflake to the Google Cloud A1 Platform model. Diagnose by examining the input data being sent to the function and comparing it to the model's expected input schema.
E) The Google Cloud AI Platform model is unavailable or experiencing issues. Diagnose by checking the Google Cloud status dashboard for AI Platform outages.

Solutions: