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SASInstitute A00-485 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Building and Assessing Regression-type Models41%- Perform generalized linear regression modeling
- Perform linear regression modeling
- Perform generalized additive modeling
- Perform nonparametric logistic regression modeling
- Explain concepts of linear models
- Assess model fit and diagnostics
Topic 2: SAS Visual Statistics Cross-functional Tasks22%- Perform model validation
- Prepare data using SAS Visual Analytics
- Use interactive group-by functionality
- Filter data used for a model
Topic 3: Model Comparison and Scoring9%- Apply models to score new data
- Compare multiple models using fit statistics
Topic 4: Building and Assessing Segmentation Models28%- Analyze and interpret cluster results
- Assess and interpret decision tree performance
- Perform supervised segmentation using decision trees
- Perform unsupervised segmentation using cluster analysis

SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

1. You want to use a generalized linear model to predict donation amount from a set of demographics predictors. Donation amount is known to be right-skewed, with many non-donors (donation amount = 0).
Which settings in the Generalized Linear Model task are most appropriate for modeling this response?

A)

B)

C)

D)


2. What is the purpose of implementing score code generated by SAS Visual Statistics?

A) To assess the distribution of residuals in the model
B) To apply the trained model to new data for predictions
C) To evaluate the performance of the scoring code
D) To calculate the p-values of predictor variables


3. When setting proper inputs for the k-means algorithm, what parameter represents the number of clusters to create?

A) K-value
B) Learning rate
C) Random seed
D) Cluster centroid initialization


4. Refer to the following exhibit:

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

A) Use the leaf ID data item in filters in other types of visualizations.
B) Use the leaf ID to rank order by posterior probabilities.
C) Use the leaf ID to identify the most important variables.
D) Use the leaf ID to identify clusters.


5. Which of the following are common distributions used in generalized linear models?

A) Poisson
B) Normal
C) Logistic
D) Exponential


Solutions:

Question # 1
Answer: C
Question # 2
Answer: B
Question # 3
Answer: A
Question # 4
Answer: A
Question # 5
Answer: A,B,C

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