Prepare for your exam certification with our A00-485 Certified SASInstitute [Q101-Q119]

admin A00-485 SASInstitute September 25, 2024

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Prepare for your exam certification with our A00-485 Certified SASInstitute

Free SASInstitute A00-485 Exam 2024 Practice Materials Collection

NO.101 When investigating leaf nodes in a decision tree, what does it reveal about the data?

The class labels assigned to each leaf node

The feature values associated with each leaf node

The decision rules leading to each leaf node

The statistical significance of each leaf node

NO.102 What can be interpreted from the Summary Table for model comparison, including statistics and variable importance?

The distribution of predictor variables

Model performance metrics and their relative importance

The number of iterations performed by each model

The correlation matrix of predictor variables

NO.103 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

NO.104 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

NO.105 What is the primary purpose of specifying the link function in logistic regression?

To control the significance level of predictor variables

To determine the distribution of the response variable

To ensure that all predictor variables are used

To establish the relationship between the linear predictor and the probability of an event

NO.106 Refer to the exhibits:

An analyst is evaluating which model to use.
What changed from Comparison 1 to Comparison 2 to select a different champion model?

Prediction Cutoff reduced from .5 to .19

Percentile changed from 5th percentile to 40th percentile

Analyst clicked on Model 2 to highlight it

Misclassification was used instead of Cumulative Lift

NO.107 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.

NO.108 Your data contains a column for Country that contains 350 unique values. There are multiple entries for some countries (Japan, JPN, japan, etc.). You want a unique value for each country.
Which is the most efficient way to correct inconsistent values?

Create a data source filter to suppress the incorrect variations of a country.

Create a calculated item using a Boolean IF…ELSE statement that changes the incorrect variations.

Create a visualization filter to suppress the incorrect variations of a country.

Create a Custom Category that combines all the variations of a country into one.

NO.109 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

NO.110 What information is conveyed by a Summary bar in model assessment?

The standard deviation of the predictor variables

The summary statistics of the response variable

The performance metrics of the model, such as AUC and KS Statistic

The distribution of residuals in the model

NO.111 What does it mean to create dummy variables in SAS Visual Data Builder?

Generating artificial data points

Converting categorical variables into binary variables

Replacing missing values with zeros

Aggregating multiple measures into one variable

NO.112 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.

NO.113 What are decision tree properties that can be defined during model construction?

The maximum depth of the tree

The minimum number of data points in a leaf node

The learning rate for gradient boosting

The number of neighbors in a k-nearest neighbors model

NO.114 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

NO.115 Which of the following criteria are commonly used for pruning a decision tree?

Minimum split count

Maximum depth

Maximum leaf nodes

Maximum tree height

NO.116 In the below linear regression results display, where would you click to access the parameter estimates?

NO.117 How does SAS Data Integration Studio facilitate scoring new data using exported score code?

By generating score code automatically during ETL processes

By providing a visual interface for connecting to external data sources

By enabling the scheduling of batch scoring jobs

By automatically tuning hyperparameters for scoring

NO.118 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.

NO.119 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.

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