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DSA-C03 - SnowPro Advanced: Data Scientist Certification Exam–Valid Valid Exam Online

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q162-Q167):

NEW QUESTION # 162
A financial services company wants to predict loan defaults. They have a table 'LOAN APPLICATIONS' with columns 'application_id', applicant_income', 'applicant_age' , and 'loan_amount'. You need to create several derived features to improve model performance.
Which of the following derived features, when used in combination, would provide the MOST comprehensive view of an applicant's financial stability and ability to repay the loan? Select all that apply

A. Calculated as 'loan_amount I applicant_age' .
B. Requires external data from a credit bureau to determine total debt, then calculated as 'total_debt / applicant_income' (Assume credit bureau integration is already in place)
C. Calculated as 'applicant_age applicant_age'.
D. Calculated as 'applicant_age / applicant_income'.
E. Calculated as 'applicant_income I loan_amount'.

Answer: A,B,E

Explanation:
The best combination provides diverse perspectives on financial stability. directly reflects the applicant's ability to cover the loan with their income. represents the loan burden relative to the applicant's age and can expose risk in younger, less established applicants. provides the most comprehensive view, including existing debt obligations from external data. "age_squared' and are less directly informative about repayment ability. They could potentially capture non-linear relationships, but 'age_squareff is more likely to introduce overfitting. relies on an external data source, making it a powerful, but potentially more complex, feature to implement.

NEW QUESTION # 163
You are investigating website session durations stored in a Snowflake table named 'WEB SESSIONS. You suspect that bot traffic is artificially inflating the average session duration. You have the following session durations (in seconds) in the 'SESSION DURATION' column: [10, 12, 15, 18, 20, 22, 25, 28, 30, 1000]. Given this data and the context of bot traffic, which measure of central tendency is MOST robust to the influence of the outlier (1000) in this dataset? Assuming you already have table and dataframe created for this analysis. (Choose ONE)

A. Geometric Mean
B. Mode
C. Trimmed mean (e.g. 10% trimmed)
D. Median
E. Mean

Answer: D

Explanation:
The median is the most robust measure of central tendency in the presence of outliers. The mean is heavily influenced by extreme values. The mode is not guaranteed to be a stable measure. Geometric mean is also not robust. Trimmed mean can be useful, it's less robust compared to Median.

NEW QUESTION # 164
You are building a model to predict loan defaults using data stored in Snowflake. As part of your feature engineering process within a Snowflake Notebook, you need to handle missing values in several columns: 'annual _ income', and You want to use a combination of imputation strategies: replace missing values with the median, 'annual_income' with the mean, and with a constant value of 0.5. You are leveraging the Snowpark DataFrame API. Which of the following code snippets correctly implements this imputation strategy?

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

Answer: B,E

Explanation:
Options A and D both correctly implement the specified imputation strategy. Option A uses 'fillna' method with respective median and mean values, calculated using 'approxQuantile' and mean for missing values.Option B uses 'na.fill' which is used in Spark, and Snowflake is not compatible. Option C calculates the median and mean, but incorrectly tries to use the local Python variables inside F.lit() functions, which are executed on the Snowflake server. Option D uses loops for column selection. Option E tries to apply a literal value within a dictionary being used to fill the missing values. This is not correct, and it's important to ensure that a correct implementation is used.

NEW QUESTION # 165
You've built a model in Snowflake to predict the likelihood of a customer clicking on an advertisement. The model outputs a probability score between 0 and 1. You want to determine the optimal threshold to use for converting these probabilities into binary predictions (click/no-click). Your business stakeholders have provided the following information: Cost of showing an ad: $0.10; Revenue generated from a click: $1.00; You have access to a table 'AD_PREDICTIONS' with columns 'CUSTOMER_ID', 'PREDICTED_PROBABILITY' , and 'ACTUAL CLICK' (1 for click, 0 for no click). Which of the following approaches would be the MOST appropriate for selecting the optimal probability threshold to maximize profit, and why?

A. Select a threshold of 0.5, as this is a common default threshold for binary classification problems.
B. Iterate through a range of probability thresholds (e.g., 0.01 to 0.99), and for each threshold, calculate the profit using SQL in Snowflake: 'SELECT SUM(CASE WHEN PREDICTED PROBABILITY threshold THEN CASE WHEN ACTUAL CLICK = 1 THEN 0.9 ELSE -0.1 END ELSE O END) AS Profit FROM AD_PREDICTIONS;' Choose the threshold that maximizes the profit.
C. Calculate the point on the ROC curve closest to the top-left corner (perfect classification) and use the corresponding threshold. This optimizes for both sensitivity and specificity.
D. Select a very high probability threshold (e.g., 0.9) to ensure that only the most likely clicks are targeted, minimizing wasted ad spend.
E. Use the precision-recall curve to find the threshold that maximizes the F1 -score, balancing precision and recall.

Answer: B

Explanation:
Option C is the most appropriate approach. While other options might seem reasonable in isolation, they don't directly optimize for profit, which is the ultimate business goal. Option C directly calculates the profit for each threshold, taking into account the cost of showing an ad and the revenue from a click. It correctly models the profit calculation: if the predicted probability is above the threshold, and there's an actual click, the profit is $1.00 (revenue) - $0.10 (cost) = $0.90. If the predicted probability is above the threshold, but there's no actual click, the loss is $0.10 (cost). All other approaches don't optimize directly for profit, based on the costs and revenues given.

NEW QUESTION # 166
You are deploying a machine learning model to Snowflake using a Python UDF. The model predicts customer churn based on a set of features. You need to handle missing values in the input data'. Which of the following methods is the MOST efficient and robust way to handle missing values within the UDF, assuming performance is critical and you don't want to modify the underlying data tables?

A. Pre-process the data in Snowflake using SQL queries to replace missing values with the mean for numerical features and the mode for categorical features before calling the UDF.
B. Implement a custom imputation strategy using 'numpy.where' within the UDF, basing the imputation value on a weighted average of other features in the row.
C. Use within the UDF to forward fill missing values. This assumes the data is ordered in a meaningful way, allowing for reasonable imputation.
D. Use within the UDF, replacing missing values with a global constant (e.g., 0) defined outside the UDF. This constant is pre-calculated based on the training dataset's missing value distribution.
E. Raise an exception within the UDF when a missing value is encountered, forcing the calling application to handle the missing values.

Answer: A

Explanation:
Pre-processing data in Snowflake with SQL for imputation offers several advantages. It allows leveraging Snowflake's compute resources for data preparation, rather than the UDF's limited resources. Handling missing values before the UDF call also simplifies the UDF code, making it more efficient and less prone to errors. Using 'fillna' within the UDF (options A, B, and C) can lead to performance bottlenecks and potential data leakage issues if not carefully managed. Raising an exception (option E) is not practical for production deployments where missing values are expected.

NEW QUESTION # 167
......

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