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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are a data scientist working with a large dataset of customer transactions stored in Snowflake. You need to identify potential fraud using statistical summaries. Which of the following approaches would be MOST effective in identifying unusual spending patterns, considering the need for scalability and performance within Snowflake?

A) Export the entire dataset to a Python environment, use Pandas to calculate the average transaction amount and standard deviation for each customer, and then identify outliers based on a fixed threshold.
B) Sample a subset of the data, calculate descriptive statistics using Snowpark Python and the 'describe()' function, and extrapolate these statistics to the entire dataset.
C) Use Snowflake's native anomaly detection functions (if available, and configured for streaming) to detect anomalies based on transaction amount and frequency, grouped by customer ID.
D) Implement a custom UDF (User-Defined Function) in Java to calculate the interquartile range (IQR) for each customer's transaction amounts and flag transactions as outliers if they are below QI - 1.5 IQR or above Q3 + 1.5 IQR.
E) Calculate the average transaction amount and standard deviation for each customer using window functions in SQL. Flag transactions that fall outside of 3 standard deviations from the customer's mean.

2. You are tasked with building a machine learning model in Python using data stored in Snowflake. You need to efficiently load a large table (100GB+) into a Pandas DataFrame for model training, minimizing memory footprint and network transfer time. You are using the Snowflake Connector for Python. Which of the following approaches would be MOST efficient for loading the data, considering potential memory limitations on your client machine and the need for data transformations during the load process?

A) Create a Snowflake view with the necessary transformations, and then load the view into a Pandas DataFrame using 'pd.read_sql()'.
B) Use 'snowsql' to unload the table to a local CSV file, then load the CSV file into a Pandas DataFrame.
C) Utilize the 'execute_stream' method of the Snowflake cursor to fetch data in chunks, apply transformations in each chunk, and append to a larger DataFrame or process iteratively without creating a large in-memory DataFrame.
D) Use the 'COPY INTO' command to unload the table to an Amazon S3 bucket and then use bot03 in your python script to fetch data from s3 and load into pandas dataframe.
E) Load the entire table into a Pandas DataFrame using with a simple 'SELECT FROM my_table' query and then perform data transformations in Pandas.

3. You are building a customer support chatbot using Snowflake Cortex and a large language model (LLM). You want to use prompt engineering to improve the chatbot's ability to answer complex questions about product features. You have a table PRODUCT DETAILS with columns 'feature_name', Which of the following prompts, when used with the COMPLETE function in Snowflake Cortex, is MOST likely to yield the best results for answering user questions about specific product features, assuming you are aiming for concise and accurate responses focused solely on providing the requested feature description and avoiding extraneous chatbot-like conversation?

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

4. You are building a customer churn prediction model in Snowflake using Snowflake ML. After training, you need to evaluate the model's performance and identify areas for improvement. Given the following table 'PREDICTIONS' contains predicted probabilities and actual churn labels, which SQL query effectively calculates both precision and recall for the churn class (where 'CHURN = 1')?

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

5. You are tasked with presenting a business case to stakeholders demonstrating the value of a new machine learning model that predicts customer churn. The model has been trained on data within Snowflake, and you have various metrics such as accuracy, precision, recall, and F I-score. You also have feature importance scores generated using a SHAP (SHapley Additive exPlanations) explainer. Which of the following visualization strategies, when combined, would MOST effectively communicate the model's performance and impact to a non-technical audience, while also providing sufficient detail for technical stakeholders?

A) A confusion matrix visualizing the true positives, true negatives, false positives, and false negatives, along with a summary plot of the SHAP values showing the impact of each feature on the model's prediction for a representative sample of customers. A line chart showing cumulative churn rate across different customer segments.
B) A ROC curve (Receiver Operating Characteristic) showing the trade-off between true positive rate and false positive rate, paired with a detailed table of all feature importance scores generated by the SHAP explainer. Present statistical summaries, such as mean and standard deviation, of the top 5 feature values, grouped by predicted churn probability.
C) A scatter plot showing the relationship between two key features identified by SHAP, colored by the model's churn prediction, and a table summarizing the model's performance metrics (accuracy, precision, recall, F I-score). Additionally, include a waterfall plot for a specific customer, illustrating how each feature contributes to the final prediction.
D) A distribution plot (e.g., histogram or KDE) of the predicted churn probabilities, segmented by actual churn status (churned vs. not churned), combined with a SHAP force plot visualizing the feature contributions for a single, randomly selected customer who churned. Add a section on potential cost savings from churn reduction.
E) A simple bar chart showing the overall accuracy score of the model alongside a table detailing the precision, recall, and F I-score. Include a word cloud of the most important features from the SHAP values.

Solutions: