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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A data scientist is using association rule mining with the Apriori algorithm on customer purchase data in Snowflake to identify product bundles. After generating the rules, they obtain the following metrics for a specific rule: Support = 0.05, Confidence = 0.7, Lift = 1.2. Consider that the overall purchase probability of the consequent (right-hand side) of the rule is 0.4. Which of the following statements are CORRECT interpretations of these metrics in the context of business recommendations for product bundling?

A) The confidence of 0.7 indicates that 70% of transactions containing the antecedent also contain the consequent.
B) The rule applies to 5% of all transactions in the dataset, meaning 5% of the transactions contain both the antecedent and the consequent.
C) The lift value of 1.2 indicates that customers are 20% more likely to purchase the consequent items when they have also purchased the antecedent items, compared to the baseline purchase probability of the consequent items.
D) Customers who purchase the items in the antecedent are 70% more likely to also purchase the items in the consequent, compared to the overall purchase probability of the consequent.
E) The lift value of 1.2 suggests a strong negative correlation between the antecedent and consequent, indicating that purchasing the antecedent items decreases the likelihood of purchasing the consequent items.

2. You are building a real-time fraud detection system using Snowpark ML and Dynamic Tables. The raw transaction data arrives continuously in a Snowflake stream. You need to create a data science pipeline that continuously transforms the data, trains a model, and scores new transactions in near real-time. Which combination of Snowflake features provides the BEST solution for achieving low latency and high throughput for this fraud detection system? Select all that apply:

A) Snowpipe with Auto-Ingest to load the raw transaction data into a staging table before processing it with Dynamic Tables.
B) Snowflake Tasks with a 'WHEN SYSTEM$STREAM HAS clause to incrementally process new transactions from the stream and update feature tables.
C) Snowpark ML User-Defined Functions (UDFs) to apply the fraud detection model to incoming transactions, executed using Snowflake's vectorized engine for optimal performance.
D) Scheduled Snowflake tasks to retrain the model every hour based on the most recent transaction data.
E) Dynamic Tables to continuously transform the raw transaction data into features required by the model, with 'WAREHOUSE SIZE set to 'X-LARGE to ensure sufficient compute resources.

3. You are training a Gradient Boosting model within Snowflake using Snowpark Python to predict customer churn. You are using the Hyperopt library for hyperparameter tuning. You want to use the function to find the best hyperparameters. You have defined your objective function, , and the search space, Which of the following is the MOST efficient and correct way to call the function within a Snowpark Python UDF to ensure the Hyperopt trials data is effectively managed and accessible for further analysis within Snowflake?

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

4. You're building a model to predict whether a user will click on an ad (binary classification: click or no-click) using Snowflake. The data is structured and includes features like user demographics, ad characteristics, and past user interactions. You've trained a logistic regression model using SNOWFLAKE.ML and are now evaluating its performance. You notice that while the overall accuracy is high (around 95%), the model performs poorly at predicting clicks (low recall for the 'click' class). Which of the following steps could you take to diagnose the issue and improve the model's ability to predict clicks, and how would you implement them using Snowflake SQL? SELECT ALL THAT APPLY.

A) Reduce the amount of training data to avoid overfitting. Overfitting is known to produce low recall for the 'click' class.
B) Generate a confusion matrix using SQL to visualize the model's performance across both classes. Example SQL:

C) Increase the complexity of the model by switching to a non-linear algorithm like Random Forest or Gradient Boosting without performing hyperparameter tuning, as more complex models always perform better.
D) Calculate precision, recall, F I-score, and AUC for the 'click' class using SQL queries to get a more detailed understanding of the model's performance on the minority class. Example:

E) Implement feature engineering by creating interaction terms or polynomial features from existing features using SQL, to capture potentially non-linear relationships between features and the target variable. Example:

5. 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) 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.
D) 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).
E) 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.

Solutions: