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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A telecom company, 'ConnectPlus', observes that the individual call durations of its customers are heavily skewed towards shorter calls, following an exponential distribution. A data science team aims to analyze call patterns and requires to perform hypothesis testing on the average call duration. Which of the following statements regarding the applicability of the Central Limit Theorem (CLT) in this scenario are correct if the sample size is sufficiently large?

A) The CLT is applicable, and the sample mean will converge to the population median.
B) The CLT is not applicable because the population distribution (call durations) is heavily skewed.
C) The CLT is applicable only if the sample size is extremely large (e.g., greater than 10,000), due to the exponential distribution's heavy tail.
D) The CLT is applicable as long as the sample size is reasonably large (typically n > 30), and the distribution of sample means will be approximately normal. The specific minimum sample size depends on the severity of the skewness.
E) The CLT is applicable, and the distribution of sample means of call durations will approximate a normal distribution, regardless of the skewness of the individual call durations.

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) Include the line 'import sys; sys._xoptions['snowflake_home'] = at the top of your UDF to point to the environment stage location.
B) 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".
C) 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'.
D) Install the packages directly into the Snowflake environment using 'CREATE OR REPLACE FUNCTION RETURNS VARCHAR ..: and a pip install command within the function.
E) Create a ZIP file containing the Python environment and upload it to a Snowflake stage.

3. You are working on a customer churn prediction model and are using Snowpark Feature Store. One of your features, is updated daily. You notice that your model's performance degrades over time, likely due to stale feature values being used during inference. You want to ensure that the model always uses the most up-to-date feature values. Which of the following strategies would be the MOST effective way to address this issue using Snowpark Feature Store and avoid model staleness during online inference?

A) Use the method on the Feature Store client during inference, ensuring that you always pass the current timestamp.
B) Define a custom User-Defined Function (UDF) in Snowflake that retrieves the 'customer_lifetime_value' from the Feature Store on demand whenever the model makes a prediction and set 'feature_retrieval_mode='fresh'S.
C) Configure the Feature Group containing to automatically refresh every hour using a scheduled Snowpark Python function.
D) Configure with the attribute to manage data staleness and use the during inference, ensuring that the model always uses recent feature values.
E) Implement a real-time feature retrieval service that directly queries the underlying Snowflake table containing the using Snowpark, bypassing the Feature Store.

4. You are using a Snowflake Notebook to build a churn prediction model. You have engineered several features, and now you want to visualize the relationship between two key features: and , segmented by the target variable 'churned' (boolean). Your goal is to create an interactive scatter plot that allows you to explore the data points and identify any potential patterns.
Which of the following approaches is most appropriate and efficient for creating this visualization within a Snowflake Notebook?

A) Use the 'snowflake-connector-python' to pull the data and use 'seaborn' to create static plots.
B) Leverage Snowflake's native support for Streamlit within the notebook to create an interactive application. Query the data directly from Snowflake within the Streamlit app and use Streamlit's plotting capabilities for visualization.
C) Create a static scatter plot using Matplotlib directly within the Snowflake Notebook by converting the data to a Pandas DataFrame. This involves pulling all relevant data into the notebook's environment before plotting.
D) Write a stored procedure in Snowflake that generates the visualization data in a specific format (e.g., JSON) and then use a JavaScript library within the notebook to render the visualization.
E) Use the Snowflake Connector for Python to fetch the data, then leverage a Python visualization library like Plotly or Bokeh to generate an interactive plot within the notebook.

5. A marketing team at 'RetailSphere' wants to segment their customer base using unstructured textual data (customer reviews) stored in a Snowflake VARIANT column named 'REVIEW TEXT within the table 'CUSTOMER REVIEWS'. They aim to identify distinct customer segments based on sentiment and topics discussed in their reviews. They want to use a Supervised Learning approach for this task. Which of the following strategies best describes the appropriate approach within Snowflake, considering performance and scalability? Assume you have pre-trained sentiment and topic models deployed as Snowflake external functions.

A) Extract the 'REVIEW TEXT column, apply sentiment analysis and topic modeling using Java within a Snowflake UDF, and then perform hierarchical clustering directly on the resulting features within Snowflake. Manually label the clusters after visual inspection.
B) Extract the ' REVIEW_TEXT column, manually categorize a small subset of reviews into predefined segments. Train a text classification model (e.g., using scikit-learn) externally, deploy it as a Snowflake external function, and then apply this function to the entire 'REVIEW TEXT column to predict segment assignments. Manually adjust cluster centroids to represent the manually labeled dataset.
C) Create a Snowflake external function to call a pre-trained sentiment analysis and topic modeling model hosted on Azure ML. Apply these functions to the REVIEW_TEXT column to generate sentiment scores and topic probabilities. Subsequently, use these features as input to an unsupervised clustering algorithm (e.g., DBSCAN) within Snowflake, relying solely on data density to define segments.
D) Create a Snowflake external function to call a pre-trained sentiment analysis and topic modeling model hosted on AWS SageMaker. Apply these functions to the ' REVIEW_TEXT column to generate sentiment scores and topic probabilities. Subsequently, use these features as input to a supervised classification model (e.g., XGBoost) also deployed as a Snowflake external function, training on a manually labeled subset of reviews.
E) Extract the column, apply sentiment analysis and topic modeling using Python within a Snowflake UDF, and then perform K-Means clustering directly on the resulting features within Snowflake. Define the labels after clustering based on the majority class of the topics and sentiments in each cluster.

Solutions: