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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. Which of the following statements about Z-tests and T-tests are generally true? Select all that apply.

A) Both Z-tests and T-tests assume that the data is non-normally distributed.
B) A T-test is generally used when the sample size is large (n > 30) and the population standard deviation is known.
C) As the sample size increases, the T-distribution approaches the standard normal (Z) distribution.
D) A Z-test requires knowing the population standard deviation, while a T-test estimates it from the sample data.
E) A T-test has fewer degrees of freedom compared to the Z-test, making it more robust to outliers.

2. You have trained a classification model in Snowflake using Snowpark ML to predict customer churn. After deploying the model, you observe that the model performs well on the training data but poorly on new, unseen data'. You suspect overfitting. Which of the following strategies can be applied within Snowflake to detect and mitigate overfitting during model validation , considering the model is already deployed and receiving inference requests through a Snowflake UDF?

A) Create shadow UDFs that score data using alternative models. Compare the performance metrics (such as accuracy, precision, recall) between the production UDF and shadow UDFs using Snowflake's query capabilities. If shadow models consistently outperform the production model on certain data segments, retrain the production model incorporating those data segments with higher weights.
B) Since the model is already deployed, the only option is to collect inference requests and compare the distributions of predicted values in each batch with the predicted values on the training set. A large difference indicates overfitting; model must be retrained outside of the validation process.
C) Calculate the Area Under the Precision-Recall Curve (AUPRC) using Snowflake SQL on both the training and validation datasets. A significant difference indicates overfitting. Then, retrain the model in Snowpark ML with added L1 or L2 regularization, adjusting the regularization strength based on validation set performance, and redeploy the UDF.
D) Implement k-fold cross-validation within the Snowpark ML training pipeline using Snowflake's distributed compute. Track the mean and standard deviation of the performance metrics (e.g., accuracy, Fl-score) across folds. A high variance suggests overfitting. Use this information to tune hyperparameters or select a simpler model architecture before deployment.
E) Monitor the UDF execution time in Snowflake. A sudden increase in execution time indicates overfitting. Use the 'EXPLAIN' command on the UDF's underlying SQL query to identify performance bottlenecks and rewrite the query for optimization.

3. 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) 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.
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) Snowpipe with Auto-Ingest to load the raw transaction data into a staging table before processing it with Dynamic Tables.
E) Scheduled Snowflake tasks to retrain the model every hour based on the most recent transaction data.

4. You are building a model to predict loan defaults using a dataset stored in Snowflake. After training your model and calculating residuals, you create a scatter plot of the residuals against the predicted values. The plot shows a cone-shaped pattern, with residuals spreading out more as the predicted values increase. Which of the following SQL queries, run within a Snowpark Python session, could be used to address the underlying issue indicated by this residual pattern, assuming the predicted values are stored in a column named and the residuals in a column named 'loan_default_residuar in a Snowflake table named 'loan_predictionds'?

A)

B)

C)

D)

E)

5. You are building a fraud detection model using Snowflake and discover a severe class imbalance (99% legitimate transactions, 1% fraudulent). You plan to use down-sampling to address this. Which of the following strategies and Snowflake SQL commands would be MOST effective and efficient for down-sampling the majority class (legitimate transactions) in a large Snowflake table named 'TRANSACTIONS before training a model using Snowpark?

A) Randomly delete rows from the 'TRANSACTIONS table where 'IS FRAUD = FALSE until the class distribution is balanced. This avoids data duplication but can be slow on large tables.
B) Create a new table 'BALANCED TRANSACTIONS' by sampling the majority class and combining it with the minority class using UNION ALLS. Use the'SAMPLE clause in Snowflake SQL for efficient sampling:

C) Use Snowpark's function with replacement to create a balanced dataset. This is efficient within the Snowpark environment but might be slower than native SQL sampling for initial data preparation.
D) Manually iterate through the 'TRANSACTIONS' table using a Snowpark 'DataFrame' and randomly select rows from the majority class. This is the most efficient approach for very large tables.
E) Create a new table 'BALANCED_TRANSACTIONS' by sampling the majority class and combining it with the minority class using 'UNION ALL'. Use the 'SAMPLE clause in Snowflake SQL for efficient sampling:

Solutions: