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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q16-Q21):

NEW QUESTION # 16
You are building a binary classification model in Snowflake to predict customer churn based on historical customer data, including demographics, purchase history, and engagement metrics. You are using the SNOWFLAKE.ML.ANOMALY package. You notice a significant class imbalance, with churn representing only 5% of your dataset. Which of the following techniques is LEAST appropriate to handle this class imbalance effectively within the SNOWFLAKE.ML framework for structured data and to improve the model's performance on the minority (churn) class?

A. Using the 'sample_weight' parameter in the 'SNOWFLAKE.ML.ANOMALY.FIT function to assign higher weights to the minority class instances during model training.
B. Adjusting the decision threshold of the trained model to optimize for a specific metric, such as precision or recall, using a validation set. This can be done by examining the probability outputs and choosing a threshold that maximizes the desired balance.
C. Downsampling the majority class to create a more balanced training dataset within Snowflake using SQL before feeding the data to the modeling function.
D. Applying a SMOTE (Synthetic Minority Over-sampling Technique) or similar oversampling technique to generate synthetic samples of the minority class before training the model outside of Snowflake, and then loading the augmented data into Snowflake for model training.
E. Using a clustering algorithm (e.g., K-Means) on the features and then training a separate binary classification model for each cluster to capture potentially different patterns of churn within different customer segments.

Answer: E

Explanation:
E is the LEAST appropriate. While clustering and training separate models per cluster can be a useful strategy for improving overall model performance by capturing heterogeneous patterns, it doesn't directly address the class imbalance problem within each cluster's dataset. Applying clustering does nothing about the class imbalance and adds unnecessary complexity. A, B, C, and D are all standard methods for handling class imbalance. A uses weighted training. B and D address resampling of the training set. C addresses the classification threshold.

NEW QUESTION # 17
A data scientist is performing exploratory data analysis on a table named 'CUSTOMER TRANSACTIONS. They need to calculate the standard deviation of transaction amounts C TRANSACTION AMOUNT) for different customer segments CCUSTOMER SEGMENT). The 'CUSTOMER SEGMENT column can contain NULL values. Which of the following SQL statements will correctly compute the standard deviation, excluding NULL transaction amounts, and handling NULL customer segments by treating them as a separate segment called 'Unknown'? Consider using Snowflake-specific functions where appropriate.

A. Option A
B. Option B
C. Option C
D. Option D
E. Option E

Answer: B,C

Explanation:
Options B and C correctly calculates the standard deviation. Option B utilizes 'NVL' , which is the equivalent of 'COALESCE or ' IFNULL', to handle NULL Customer Segment values, and 'STDDEV_SAMP' for sample standard deviation, which is generally the correct function to use when dealing with a sample of the entire population. Option C also uses 'COALESCE and utilizes the 'STDDEV POP function, which returns the population standard deviation, assuming the data represents the whole population. Option A uses IFNULL, which works, and STDDEV, which is an alias for either STDDEV SAMP or STDDEV POP. The exact behavior will depend on session variable setting. Option D also uses 'CASE WHEN' construct which works to identify Unknown segments. STDDEV is again aliased. Option E calculates the variance and not Standard deviation.

NEW QUESTION # 18
You are building a predictive model for customer churn using linear regression in Snowflake. You have identified several features, including 'CUSTOMER AGE', 'MONTHLY SPEND', and 'NUM CALLS'. After performing an initial linear regression, you suspect that the relationship between 'CUSTOMER AGE and churn is not linear and that older customers might churn at a different rate than younger customers. You want to introduce a polynomial feature of "CUSTOMER AGE (specifically, 'CUSTOMER AGE SQUARED') to your regression model within Snowflake SQL before further analysis with python and Snowpark. How can you BEST create this new feature in a robust and maintainable way directly within Snowflake?

A. Option B
B. Option A
C. Option C
D. Option D
E. Option E

Answer: C

Explanation:
Creating a VIEW (option C) is the BEST approach for several reasons. It doesn't modify the underlying data, which is crucial for data govemance and prevents unintended side effects. The feature is calculated on-the-fly whenever the view is queried, ensuring that the feature is always up-to-date if the underlying changes. Options A, D, and E permanently alter the table, potentially leading to data redundancy and requiring manual updates if the column changes. Option B creates a temporary table, which is suitable for short-lived experiments but not ideal for a feature that will be used repeatedly. Using 2) is equivalent to CUSTOMER_AGE CUSTOMER_AGE. Views are efficient because Snowflake's query optimizer can often push down computations into the underlying table. Option C also avoids needing to manage the lifecycle of updated calculated columns.

NEW QUESTION # 19
You are developing a fraud detection model in Snowflake. You've identified that transaction amounts and transaction frequency are key features. You observe that the transaction amounts are heavily right-skewed and the transaction frequencies have outliers. Furthermore, the model needs to be robust against seasonal variations in transaction frequency. Which of the following feature engineering steps, when applied in sequence, would be MOST appropriate to handle these data characteristics effectively?

A. 1. Apply a logarithmic transformation to the transaction amounts. 2. Apply a Winsorization technique to the transaction frequencies to handle outliers. 3. Calculate a rolling average of transaction frequency over a 7-day window.
B. 1. Apply a Box-Cox transformation to the transaction amounts. 2. Apply a quantile-based transformation (e.g., using NTILE) to the transaction frequencies to map them to a uniform distribution. 3. Calculate the difference between the current transaction frequency and the average transaction frequency for that day of the week over the past year.
C. 1. Apply min-max scaling to the transaction amounts. 2. Remove outliers in transaction frequency using the Interquartile Range (IQR) method. 3. Calculate the cumulative sum of transaction frequencies.
D. 1. Apply a square root transformation to the transaction amounts. 2. Standardize the transaction frequencies using Z-score normalization. 3. Create dummy variables for the day of the week.
E. 1. Apply a logarithmic transformation to the transaction amounts. 2. Replace outliers in transaction frequency with the mean value. 3. Create lag features of transaction frequency for the previous 7 days.

Answer: B

Explanation:
Option C is the most comprehensive solution. Box-Cox transformation is effective for skewed data and can handle negative values (if applicable after shifting). Quantile-based transformation maps the transaction frequencies to a uniform distribution, mitigating the impact of outliers. Calculating the difference between the current transaction frequency and the historical average for that day of the week effectively removes seasonality. Logarithmic transformation (A) is a good alternative to Box-Cox but might not be optimal for all skewness types. Winsorization (A) reduces the impact of outliers but doesn't necessarily normalize the data distribution. Standardization (B) is suitable if the data follows a normal distribution, but may not be effective with heavy outliers. Min-max scaling (D) preserves the data distribution, so it is not a remedy for skewed data. Removing outliers (D) can lead to information loss. Replacing outliers with the mean (E) can distort the data distribution.

NEW QUESTION # 20
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'?

Answer: A

Explanation:
A cone-shaped pattern in the residuals plot (heteroscedasticity) indicates that the variance of the errors is not constant. Applying a transformation like Box-Cox to the target variable before retraining the model (Option D) is the most appropriate way to address this. Option A attempts to filter outliers based on the residuals, but does not address the heteroscedasticity itself and requires statistical functions unavailable within standard SQL. Option B attempts to take the natural log of the residuals, which is nonsensical as residuals can be negative. Option C attempts to filter based on the rank of residuals, which is similarly unhelpful, does not fix the problem, and uses inappropriate outlier removal with SQL QUALIFY clause. Option E scaling the features might sometimes improve model performance, but it does not directly address heteroscedasticity.

NEW QUESTION # 21
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