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: Jul 22, 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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When preparing data for a predictive modeling project, a data scientist notices that the categorical variable 'payment_type' with four categories ('credit card', 'debit card', 'paypal', 'other') exhibits a high degree of variability in the outcome variable (purchase amount). To improve the model's predictive accuracy, what strategy can the data scientist use to handle the 'payment_type' variable?
Answer: B
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Question 2
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You are conducting a time series analysis and need to estimate the parameters of an ARIMA (Autoregressive Integrated Moving Average) model to forecast future sales. Given that the data show signs of non-stationarity and seasonality, which parameter estimation method should you use for the best results?
Answer: B
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Question 3
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You have built several predictive models using training data and now you want to assess the models' performance using validation data. Which measure is NOT appropriate for comparing model performance in terms of model bias for continuous outcomes?
Answer: B
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You have built several predictive models using training data and now you want to assess the models' performance using validation data. Which measure is NOT appropriate for comparing model performance in terms of model bias for continuous outcomes?
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Question 4
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A binary classifier is used to predict a rare event. Its performance is summarized in the following confusion matrix: | | Predicted Negative | Predicted Positive | |-||| | Actual Negative | 9750 | 250 | | Actual Positive | 25 | 75 | Given the model's performance, what is the False Positive Rate (FPR) of the classifier?
Answer: A
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A binary classifier is used to predict a rare event. Its performance is summarized in the following confusion matrix: | | Predicted Negative | Predicted Positive | |-||| | Actual Negative | 9750 | 250 | | Actual Positive | 25 | 75 | Given the model's performance, what is the False Positive Rate (FPR) of the classifier?
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Question 5
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When preparing to score a new dataset with a predictive model you have previously built, you notice that one of the predictor variables has a higher rate of missing values than in the training set used to build the model. What potential issue should you be wary of?
Answer: B
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When preparing to score a new dataset with a predictive model you have previously built, you notice that one of the predictor variables has a higher rate of missing values than in the training set used to build the model. What potential issue should you be wary of?