A00-485 Modeling Using SAS Visual Statistics Exam Dumps

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A00-485 Modeling Using SAS Visual Statistics exam is easy for you to pass now. There are 55-60 multiple choice and short-answer questions in real A00-485 exam, and you have 110 minutes to finish the test. The required passing score for A00-485 exam is 68%. The certification is valid for 5 years. We have cracked the latest Modeling Using SAS Visual Statistics A00-485 exam dumps, which are valuable in your preparation. Free Modeling Using SAS Visual Statistics A00-485 exam dumps are available below.

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1. How can you export score code generated by SAS Visual Statistics for a trained model?

By copying and pasting it into a text editor

By exporting it as a standalone script file

By printing it directly from the SAS Visual Statistics interface

By exporting it as a PDF document

2. Refer to the exhibit:

Which is the modeling approach that should be used when fitting the Target Gift Amount variable?

Linear regression model with Interaction effects.

Generalized linear model with a Poisson distribution and Identity link.

Generalized linear model with a Normal distribution and Log Link.

Logistic regression model.

3. When evaluating a Misclassification plot, what does it typically show?

The distribution of predictor variables in the dataset

The relationship between two predictor variables

The trade-off between false positives and false negatives at different thresholds

The p-values associated with the predictor variables

4. In SAS Visual Analytics, which of the following tasks involves converting a categorical variable into a numerical one?

Aggregating a measure

Creating a dummy variable

Replacing dirty data

Transforming a variable

5. You perform a logistic regression on a multinomial response variable in SAS Visual Statistics that has 3 levels: Small, Medium, Large. "Large" is specified as the event.

Which statement is true?

The other levels are grouped into one non-event.

An ordinal logistic regression is performed.

A multinomial logistic regression is performed.

The other levels are offset to account for exposure.

6. When building a cluster analysis in SAS Visual Statistics, which of the following algorithms is commonly used for partitioning data into clusters?

Principal Component Analysis (PCA)

Linear Regression

K-means

Decision Tree

7. How is a multinomial response variable used in SAS Visual Statistics when building a logistic regression model?

It is used as a predictor variable.

It is used as an event variable.

It is used to define the outcome categories.

It is not used in logistic regression modeling.

8. Assuming the event level is the same for both models, which pair of models can be compared?

A)

Option A

Option B

Option C

Option D

9. In cluster analysis, what does the term "centroid" refer to?

The total number of data points in a cluster

The center point of a cluster in the feature space

The number of variables used in the analysis

The minimum distance between two clusters

10. What is meant by "informative missingness" in the context of linear regression?

Missing data that is unrelated to the outcome variable

Missing data that is randomly distributed across observations

Missing data that provides valuable information about the outcome

Missing data that leads to multicollinearity among predictors

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11. How can you interpret a Tree Map in the context of a decision tree?

It visualizes the distribution of data points in leaf nodes.

It displays the hierarchical structure of the decision tree.

It shows the decision boundaries between classes.

It summarizes the performance metrics of the decision tree.

12. Which modeling technique automatically includes missing values?

Linear regression

Decision tree

Logistic regression

Generalized linear model

13. When creating a Linear Regression, Category Variables can be used for Classification Effects and

Response

Continuous Effects

Group-by

Frequency

14. Explain what an icicle plot represents in decision tree analysis.

It displays the hierarchical structure of the decision tree.

It visualizes the distribution of data points in each leaf node.

It shows the feature importance for each variable in the tree.

It summarizes the performance metrics of the decision tree.

15. Refer to the following exhibit:

After creating a decision tree in SAS Visual Statistics, what is the value in deriving a Leaf ID Variable?

Use the leaf ID data item in filters in other types of visualizations.

Use the leaf ID to rank order by posterior probabilities.

Use the leaf ID to identify clusters.

Use the leaf ID to identify the most important variables.

16. In the context of linear regression, what does the term "linearity" refer to?

The use of a straight line to model relationships

The absence of any relationship between variables

The requirement for variables to be categorical

The use of polynomial functions for modeling

17. What is one advantage of using SAS Studio to score new data with exported score code?

It requires no additional software installation.

It provides advanced visualization capabilities.

It allows for direct integration with Hadoop clusters.

It automatically optimizes the scoring process.

18. What are some properties that can be defined in a linear regression model?

Learning rate and number of iterations

Coefficients, p-values, and R-squared

Maximum depth and minimum leaf size

Confusion matrix and accuracy

19. What is the primary purpose of scoring functionality in SAS Visual Statistics?

To evaluate model performance on a test dataset

To generate code for deploying models in production

To assess the distribution of predictor variables

To select the best model for a given dataset

20. How can score code be implemented to score new data using SAS Enterprise Miner?

By pasting the code into a web browser

By importing the code into SAS Data Integration Studio

By creating a scoring node in a SAS Enterprise Miner process flow

By manually executing the code in a command-line interface

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21. What are the essentials of logistic regression?

Modeling binary or ordinal outcomes

Estimating probabilities of categorical outcomes

Assuming a linear relationship between predictors and response

Using the logit link function to model the probability of an event

22. When assigning roles in a GLM, what does the "offset" variable typically represent?

A predictor variable

The target variable

A variable with a known constant coefficient

A variable used to adjust the intercept

23. How is prediction typically performed in logistic regression?

By calculating the mean of the response variable

By estimating the probability of an event for each observation

By fitting a straight line through the data points

By maximizing the likelihood of the response variable

24. What properties can be assigned when comparing models in SAS Visual Statistics?

The number of observations in the dataset

The variables to include in the model comparison

The learning rate for gradient boosting models

The color scheme for visualization

25. When implementing score code in SAS Enterprise Guide, which step is typically involved in the process?

Dragging and dropping the score code file into the project

Running a SQL query to generate predictions

Opening the code file and manually executing it

Converting the code to a macro for automation

26. In a generalized linear model, what is the role of the link function in transforming the linear predictor?

It determines the distribution of the response variable.

It scales the predictor variables.

It ensures that the linear predictor is always positive.

It maps the linear predictor to the range of the response variable.

27. A champion model has been selected and you want to export the score code.

Score code is exported as:

PMML

SAS DS2

SAS Data Step

28. When specifying Cluster Roles for Cluster Analysis in SAS Visual Statistics, which condition is correct?

Interaction items are allowed.

A target variable is required.

At least two variables are required.

Category items are not allowed.

29. What can you interpret from the Assessment panel in a decision tree analysis?

The tree's structural properties, such as depth and number of nodes

The tree's training and validation performance metrics

The decision boundaries between leaf nodes

The feature importance for each variable in the tree

30. In SAS Visual Analytics, what can you use to exclude selections and filter data in a visualization?

Filter containers

Exclude option in filter dialogs

Filtering using the toolbar options

Interactive filters in the visualization

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31. You would like to see the minimum and maximum values for all of your measures so that you can filter variables as needed.

Which is the most efficient way to do that?

Create aggregated measures using the Min and Max aggregations.

Select a histogram object for each measure.

Select View Measure Details within the Actions menu to the right of the dataset name.

Create a calculated item subtracting the Min aggregation from the Max aggregation.

32. When would you typically use a linear regression model instead of a generalized linear model (GLM)?

When the dependent variable is binary

When there is no assumption of linearity in the data

When dealing with count data

When the goal is to model categorical outcomes

33. How is the KS Statistic typically used when interpreting an ROC chart?

To measure the area under the ROC curve

To assess the balance between sensitivity and specificity

To identify the optimal prediction cut-off threshold

To evaluate the overall performance of a classification model

34. Interactions can be created and added to a linear regression model.

Which statement is true about interactions?

An interaction can be created for a squared category variable.

Measures and categories can be combined to create an interaction.

Each interaction is limited to 3 effects or fewer.

The maximum number of interactions allowed in a model is 3.

35. Your company has a dataset that represents global sales. You are a part of a team of analysts that each have responsibility for a certain region of the world. You decide to create a data source filter to suppress every region but yours.

What effect will this have on any new explorations that your teammates create?

It will delete all observations that do not match your region.

It will have no effect on any observations in the dataset.

It will suppress all observations that do not match your region.

It will suppress all observations that do not match their corresponding region.

36. Which statement is true for negative binomial and Poisson regression models?

Poisson regression models are used for count data, and negative binomial models are used for binary responses.

The canonical link function for Poisson regression is the log, while for negative binomial it is the logit.

Poisson regression is a special case of negative binomial regression.

Negative binomial models accommodate negative integers while Poisson regression does not.

37. In SAS Visual Statistics, which role is typically assigned to variables in cluster analysis?

Target

Predictor

Input

Output

38. Which statement is true regarding the Cluster Analysis in SAS Visual Statistics?

An observation can belong to more than one cluster.

Clusters may overlap in the Cluster Matrix plot.

SAS Visual Statistics creates a measure variable containing the cluster ID when you derive a Cluster ID variable.

You can derive a Cluster ID variable by right clicking on the Parallel Coordinates plot.

39. How can predicted values be derived and described in terms of predicted probabilities in SAS Visual Statistics?

By calculating the mean of the predictor variables

By fitting a linear regression model to the data

By applying the link function to the linear predictor

By summing the residuals of the model

40. Given a scenario, what fit statistic can be used to select a champion model in model comparison?

The p-value of the intercept term

The R-squared value

The AUC (Area Under the Curve)

The learning rate of the model

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41. Which statement is TRUE regarding a generalized additive model (GAM) in SAS Visual Analytics?

GAM assumes a strict linear relationship between the predictors and the response function.

The roughness penalty controls the balance between goodness of fit and the roughness of the spline curve.

Specification of a spline effect is optional.

A larger maximum degrees of freedom for the univariate spline term enforces a less complex fit.

42. In linear regression, what does the coefficient of determination (R-squared) measure?

The degree of multicollinearity among predictors

The goodness of fit of the model to the data

The impact of outliers on the model

The p-value associated with the intercept term

43. What information can you interpret from a Cluster Matrix?

The number of data points in each cluster

The proximity or similarity between clusters

The cluster centroids

The order of cluster creation

44. Refer to the exhibit:

Based on the Validation Data in the Model Comparison output which conclusions are valid? (Choose two.)

Logistic Regression model would be selected at lift=20

Decision Tree model would be selected at lift=5

Logistic Regression model would be selected at lift=5

Decision Tree model would be selected at lift=20

45. When reviewing outlier details in linear regression, what factors should be considered when deciding whether to exclude outliers?

The magnitude of the outlier and its impact on the model

The number of outliers in the dataset

The variable roles assigned to outliers

The p-values associated with the outliers

46. Given a scenario where the response variable represents the time until an event occurs, what distribution and link function might be appropriate for modeling?

Poisson distribution with a log link function

Normal distribution with an identity link function

Exponential distribution with a log link function

Logistic distribution with a logit link function

47. Cluster Analysis in SAS Visual Statistics is categorized as which type of machine learning?

Supervised learning

Semi-supervised learning

Unsupervised learning

Reinforcement learning

48. Which model does not produce score code?

Decision Tree using interactive mode

Regression using interaction effects

Regression using the group by option

Decision Tree using the rapid growth option

49. Refer to the exhibit from a linear regression model in SAS Visual Statistics.

Based on the table above and assuming a significance level of 0.05, what can be concluded about the linear regression model?

The Intercept is an important predictor of the response.

RestPulse is a significant predictor of the response.

For one one-unit increase in RunTime, there is an expected increase in the response of 2.6287.

For a .03696 unit decrease in RunPulse, there is an expected one-unit increase in the response.

50. Given an Influence Plot generated in a Linear Regression within SAS Visual Statistics, which diagnostic can be used to quantify leverage?

Likelihood Displacement

AIC

Adjusted R-Squared

Concordance

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51. How can you appropriately change the number of clusters in a k-means cluster analysis?

By adjusting the learning rate

By modifying the random seed

By changing the K-value

By increasing the number of iterations

52. Refer to the exhibit from SAS Visual Statistics:

What can be concluded from the lift plot shown in the exhibit?

The 20% of cases with the highest predicted probability of the modeled event level is expected to capture 2.75 times as many events as a random sample of the same size.

The response rate for the best 20% is predicted to increase 5 times under the fitted model.

The 20% of cases with the lowest predicted probability of the modeled event level is predicted to capture 2.75 times as many events as a random sample of 20%.

The response rate for the best 20% is predicted to be 2.75 times greater than the sample fitted in the model.

53. What is the primary goal of linear models in statistical modeling?

To predict categorical outcomes

To capture non-linear relationships in data

To establish a linear relationship between variables

To classify data into distinct groups

54. What is the primary benefit of exporting score code for model deployment?

It allows for model performance assessment on a test dataset.

It enables scoring of new data without the need for SAS Visual Statistics.

It provides insights into the distribution of predictor variables.

It automatically generates new predictor variables.

55. In decision tree modeling, what is the purpose of setting a minimum split count?

To prevent any splits in the tree

To determine the maximum depth of the tree

To specify the minimum number of data points required to perform a split

To control the learning rate of the model

56. Refer to the fit summary from in the exhibit below.

What can be concluded from the fit summary?

Job Type is not a significant predictor in this model.

Job Type = Office has no important variables associated with it.

Debt to income ratio is a significant predictor when Job Type = Mgr.

Debt to income ratio is a significant predictor when Job Type = Sales.

57. Refer to the exhibit:

A cluster has been created with four variables selected on the roles tab.

Select the set of Parallel Coordinates Properties that would display the image in the exhibit.

A)

Option A

Option B

Option C

Option D

A00-485 Modeling Using SAS Visual Statistics Exam Dumps

A00-485 Modeling Using SAS Visual Statistics Exam Dumps

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