Free SAS Institute A00-225 Exam Questions
Absolute Free A00-225 Exam Practice for Comprehensive Preparation
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SAS Institute A00-225 Exam Questions
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Provided By: SAS Institute
- Exam: SAS Advanced Predictive Modeling
- Certification: SAS Administration
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Total Questions: 347
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Updated On: Apr 15, 2026
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Rated: 4.9 |
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Online Users: 694
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Question 1
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You have built a logistic regression model using PROC LOGISTIC to predict the probability of customer churn based on several predictors. You want to evaluate the performance of the model by analyzing the Receiver Operating Characteristic (ROC) curve. To score a validation dataset and produce the ROC curve, which statement correctly implements the OUTROC option in the SCORE statement?
Answer: C
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You have built a logistic regression model using PROC LOGISTIC to predict the probability of customer churn based on several predictors. You want to evaluate the performance of the model by analyzing the Receiver Operating Characteristic (ROC) curve. To score a validation dataset and produce the ROC curve, which statement correctly implements the OUTROC option in the SCORE statement?
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Question 2
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A data scientist is using the SAS system to preprocess data for predictive modeling. They need to convert a character variable named `char_date` in the format 'yyyy-mm-dd' to a SAS date variable. Which INPUT statement below correctly performs this conversion?
Answer: A
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A data scientist is using the SAS system to preprocess data for predictive modeling. They need to convert a character variable named `char_date` in the format 'yyyy-mm-dd' to a SAS date variable. Which INPUT statement below correctly performs this conversion?
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Question 3
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You are analyzing a dataset with a linear regression model to predict sales revenue based on multiple input variables. To prevent overfitting, you decide to include a penalty for including too many variables in the model. Which property adjustment are you most likely to use?
Answer: B
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You are analyzing a dataset with a linear regression model to predict sales revenue based on multiple input variables. To prevent overfitting, you decide to include a penalty for including too many variables in the model. Which property adjustment are you most likely to use?
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Question 4
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As a data scientist, you have built three predictive models to forecast the risk of a rare event occurring within a patient group. To evaluate these models, you have computed several fit statistics. Consider the following statistics for the models: Model A: - BIC: 182 - AIC: 175 - KS: 0.65 - Brier Score: 0.12 Model B: - BIC: 185 - AIC: 178 - KS: 0.60 Brier Score: 0.11 Model C: - BIC: 180 - AIC: 182 - KS: 0.80 - Brier Score: 0.09 Assuming that the most important criteria for model selection is the prediction accuracy of the rare event and considering the disease is highly imbalanced, which model should you recommend?
Answer: C
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As a data scientist, you have built three predictive models to forecast the risk of a rare event occurring within a patient group. To evaluate these models, you have computed several fit statistics. Consider the following statistics for the models: Model A: - BIC: 182 - AIC: 175 - KS: 0.65 - Brier Score: 0.12 Model B: - BIC: 185 - AIC: 178 - KS: 0.60 Brier Score: 0.11 Model C: - BIC: 180 - AIC: 182 - KS: 0.80 - Brier Score: 0.09 Assuming that the most important criteria for model selection is the prediction accuracy of the rare event and considering the disease is highly imbalanced, which model should you recommend?
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Question 5
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When preparing input data for a predictive model, which of the following issues is likely to cause the most significant problems in the performance of the model?
Answer: C
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