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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

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

2. You are tasked with fine-tuning a Snowflake Cortex LLM model using your own labeled dataset to improve its performance on a specific sentiment analysis task related to customer reviews. You have already created a Snowflake stage 'my_stage' and uploaded your labeled data in CSV format to this stage. The labeled data contains two columns: 'review_text' and 'sentiment' (values: 'positive', 'negative', 'neutral'). Which of the following SQL commands, or sequences of commands, is MOST appropriate to initiate the fine-tuning process using the 'SNOWFLAKE.ML.FINETUNE LLM' function? Assume you have already set the necessary permissions for your role to access the model and stage.

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

3. You have deployed a fraud detection model in Snowflake and are monitoring its performance. The initial AUC was 0.92. After a month, you observe the AUC has dropped to 0.78. You suspect data drift. Which of the following steps should you take FIRST to investigate and address this performance degradation, focusing on efficient resource utilization within Snowflake?

A) Immediately retrain the model using the entire dataset available, scheduling a Snowpark Python UDF to perform the training.
B) Deploy a new model version with a higher classification threshold to compensate for the increased false positives.
C) Increase the complexity of the existing model architecture by adding more layers to the neural network to improve its adaptability.
D) Analyze the distributions of key features in the current production data compared to the training data using Snowflake SQL queries and visualization tools. Specifically compare the distributions of features such as transaction amount and time of day. Then, if drift is confirmed, retrain using updated data.
E) Delete the existing model and deploy a pre-trained, generic fraud detection model obtained from a public repository.

4. You are analyzing customer transaction data in Snowflake to identify fraudulent activities. The 'TRANSACTION AMOUNT' column exhibits a right-skewed distribution. Which of the following Snowflake queries is MOST effective in identifying outliers based on the Interquartile Range (IQR) method, specifically targeting unusually large transaction amounts? Assume IQR is already calculated as variable and QI as and Q3 as in snowflake session.

A) SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION_AMOUNT < qi - (1.5 iqr);
B) SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION_AMOUNT > (SELECT WITHIN GROUP (ORDER BY TRANSACTION_AMOUNT) FROM TRANSACTIONS);
C) SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION_AMOUNT > (SELECT + 3 FROM TRANSACTIONS);
D) SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION AMOUNT > q3 + (1.5 iqr);
E) SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION_AMOUNT > (SELECT MEDIAN(TRANSACTION AMOUNT) FROM TRANSACTIONS);

5. A pharmaceutical company is testing a new drug to lower blood pressure. They conduct a clinical trial with 200 patients. After treatment, the sample mean reduction in systolic blood pressure is 10 mmHg, with a sample standard deviation of 15 mmHg. You want to construct a 99% confidence interval for the true mean reduction in systolic blood pressure. Which of the following statements is most accurate concerning the appropriate distribution and critical value to use?

A) Use a z-distribution because we are estimating mean, and use a critical value of 1.96.
B) Use a t-distribution with 200 degrees of freedom, and the critical value is close to 2.576.
C) Use a z-distribution because the sample size is large (n > 30), and the critical value is approximately 2.576.
D) Use a chi-squared distribution with 199 degrees of freedom.
E) Use a t-distribution with 199 degrees of freedom, and the critical value is slightly larger than 2.576.

Solutions: