SASInstitute A00-255 考試概覽:

認證廠商:	SASInstitute
考試名稱:	SAS Predictive Modeling Using SAS Enterprise Miner 14
考試代碼:	A00-255
證照有效期限:	考取後永久有效
及格分數:	725（評分範圍200–1000）
考試形式:	選擇題, 簡答題, 實作表現題
支援語言:	English
相關認證:	SAS Certified Advanced Analytics Professional
實際考試題數:	55–60
考試費用:	250美元
考試時間:	165 minutes
推薦課程:	SAS Enterprise Miner訓練課程
考試報名:	SAS認證考試報名 Pearson VUE考試報名
範例考題:	SASInstitute A00-255 範例考題
考試方式:	可選擇線上監考模式，或至Pearson VUE考場應試
必備條件:	建議具備：SAS Enterprise Miner操作經驗、資料探勘與預測建模相關概念；無強制報考資格限制
官方大綱網址:	https://www.sas.com/en_us/certification/credentials/advanced-analytics/predictive-modeler-14.html

SASInstitute A00-255 考試大綱主題:

章節	權重	目標
主題 1: 預測模型之評估與應用	25–30%	- 進行模型評分與部署上線 - 透過損益分析與比較評估模型效能 - 套用合適的適配度統計指標 - 針對過度取樣與抽樣方法進行調整
主題 2: 資料來源	20–25%	- 修改並準備建模所需的原始資料 - 從SAS資料表建立資料來源 - 探索與評估資料來源
主題 3: 建構預測模型	35–40%	- 運用迴歸分析技術建立模型 - 運用決策樹建立模型 - 運用類神經網路建立模型 - 理解預測建模相關概念
主題 4: 模式分析	10–15%	- 解讀模式探索的分析結果 - 辨識群集與分群結果

最新的 SAS Institute SAS A00-255 免費考試真題：

1. Open the diagram labeled Practice A within the project labeled Practice A. Perform the following in SAS Enterprise Miner:

1. Set the Clustering method to Average.
2. Run the Cluster node.
What is the Importance statistic for MTGBal (Mortgage Balance)?
Response:

A) 0.42541
B) 0.42667
C) 0.60485
D) 0.32959

2. Assume in a data mining project that the task is to predict rankings of a target variable as accurately as possible. Which of the following should be used to judge prediction models?
Response:

A) KS statistic
B) average squared error
C) Gini coefficient
D) misclassification

3. Choose the correct statement that illustrates Decision Tree Split Search for continuous (interval) inputs:
Select one:
Response:

A) The variable goes through a non-linear transformation, and the transformed variable is used for testing.
B) The variable goes through a binning process, the bins are weighted based on the proportion of events in each bin, and then finally tested as an optimal split point.
C) Each unique value has the potential of being the optimal split point, except for the extreme observation.
D) Each unique value has the potential of being the optimal split point.

4. Perform these tasks in SAS Enterprise Miner:
Add a Decision Tree node, as shown below. (Make sure you use only default options in the Decision Tree node.)

Run the Decision Tree node.
Suppose that the data has been oversampled and the probability that TARGET=1 is 0.10 in the population. Incorporate the above scenario and run the Decision Tree node again.
What is the misclassification rate in the validation data set?
Response:

A) 0.10
B) 0.154788
C) 0.162252
D) 0.157016

5. Suppose your input variables have missing values. Before running a decision tree with these input variables, you should do which of the following?
Response:

A) impute only interval variables using the Tree method but do not impute the class variables
B) impute all missing values using the Tree method
C) impute only class variables using the Tree method but do not impute the interval variables
D) not impute any missing values because trees can handle them.

問題與答案：

問題 #1
答案： C

問題 #2
答案： C

問題 #3
答案： D

問題 #4
答案： D

問題 #5
答案： D