[2025年04月04日] 最新でリアルなA00-255試験問題集解答
あなたを簡単に合格させるA00-255試験問と正確なSAS Predictive Modeling Using SAS Enterprise Miner 14のPDF問題
SASINSTITUTE A00-255（SAS Enterprise Miner 14を使用したSAS予測モデリング）認証試験は、予測分析の概念を実際のシナリオに適用するために必要なスキルを個人に提供することに焦点を当てた世界的に認められた認定試験です。この認定プログラムは、SAS Enterprise Miner 14を活用して正確な結果をもたらす予測モデルを構築する能力を実証することにより、専門家がキャリアの可能性を最大化するのに役立つように設計されています。この試験は、意欲的なデータアナリスト、予測モデルビルダー、およびデータマイナー向けの重要なベンチマークの1つと考えられています。
質問 # 48
Sometimes in predictive modeling we build models using a sample that has a primary outcome proportion different from true population proportion. This is usually done when the ratio of primary to secondary outcomes in a binary target variable in the population is close to which of the following?
Response:
- A. 0
- B. 1.2
- C. 0.8
- D. 0.05
正解：D
質問 # 49
Impute the missing values for the variable TLSum using the Tree method. What is the mean of the new variable (with the imputed values)?
Response:
- A. 30,000-39,999.99
- B. less than 19,999.99
- C. 40,000 or higher
- D. 20,000-29,999.99
正解：B
質問 # 50
Refer to the graphs shown below. The graphs are from a study of response rate to a marketing campaign.
How much more likely are the top 20% of targeted respondents to purchase the product than a randomly selected sample?
Select one:
Response:
- A. 30%
- B. 140%
- C. 25%
- D. 60%
正解：B
質問 # 51
For the variable InqCnt06, replace all values over 10.1 with the value 10. How many values are replaced?
Response:
- A. 0-99
- B. 100-149
- C. 200 or higher
- D. 150-199
正解：B
質問 # 52
Perform these tasks in SAS Enterprise Miner:
- Add a Decision Tree node after the Impute node with TARGET as the dependent variable and all other input variables as independent variables (main effects only). Configure the decision tree to use 1 for Number of Surrogate Rules and Largest for Method in Subtree. Do not change any other property of the Decision Tree node.
- Add another Neural Network node after the decision tree with TARGET as the dependent variable and all other input variables as independent variables (main effects only). Configure the Neural Network model to use Average Error for Model Selection Criterion. Do not change any other property for the Neural Network node. Run the process flow.
In the validation data, the lift corresponding to the fourth decile is in which of the following ranges?
Response:
- A. 1.5-1.74999
- B. 1.25-.49999
- C. 0-1.24999
- D. 1.75 or more
正解：A
質問 # 53
-> Add a Decision Tree node after the Impute node with TARGET as the dependent variable and all other input variables as independent variables (main effects only).
- Allow for 1 substitute rule in case the variable for the primary splitting rule is missing.
- Disable pruning for the decision tree.
-> Add another Neural Network node after the decision tree with TARGET as the dependent variable and all other input variables as independent variables (main effects only).
- Configure the Neural Network model to use Average Error for Model Selection Criterion.
-> Run the process flow.
What is the number of input variables being used by the Neural Network Model?
Enter your numeric answer in the space below:
Response:
- A. 0
- B. 1
- C. 2
- D. 3
正解：B
質問 # 54
Which of the following is not true about results produced by the Regression node?
Response:
- A. Model Information provides you with information that includes the number of target categories and the number of model parameters.
- B. Fit Statistics can provide information that affects decision predictions, but does not affect estimate predictions.
- C. Type 3 Analysis of Effects provides you with information about the number of parameters that each input contributes to the model.
- D. Variable Summary information identifies the roles of variables used by the Regression node.
正解：B
質問 # 55
Which method of input selection for regression analysis evaluates the statistical significance of all included inputs after each input is added?
Select one:
Response:
- A. Stepwise
- B. Simple
- C. Forward
- D. Backward
正解：A
質問 # 56
Perform this task using SAS Enterprise Miner:
Continue to use the same diagram. Use an Ensemble node (configure using default options) in SAS Enterprise Miner to combine all four models.
Compare the performance of the ensemble and the four models using average squared error in the validation data. Which is the best model in this comparison?
Response:
- A. Regression
- B. Decision Tree
- C. Neural Network
- D. Ensemble
正解：D
質問 # 57
What is the average squared error in the training data?
Response:
- A. 0.131208
- B. 0.133665
- C. 0.131709
- D. 0.131583
正解：D
質問 # 58
In SAS Enterprise Miner's Decision Tree node, which of the following types of target variable can be used?
Response:
- A. all of the above
- B. interval
- C. nominal with any number of categories
- D. binary
正解：A
質問 # 59
You are building a model to identify fraud. Your model will produce predictions that can be interpreted as the probability of fraud. You will pass on the top 100 scoring cases to management for investigation. Assume that you have sufficient data to hold out a validation and test data set for model evaluation.
Which selection would represent a reasonable ordering of fit statistics (best to worst) for this situation?
Select one:
Response:
- A. Misclassification rate, ROC index, ASE
- B. ASE, Lift for the top 10%, ROC index
- C. ROC index, ASE, misclassification rate
- D. R-square, AIC, K-S statistic
正解：C
質問 # 60
A multilayer perceptron neural network is using three interval inputs to model one interval target (outcome). The neural network has ten hidden units and one hidden layer. How many weights, including biases are being estimated?
You may use a calculator for this question. On the certification exam, an on-screen calculator is provided for you.
Select one:
Response:
- A. 0
- B. 1
- C. 2
- D. 3
正解：B
質問 # 61
Perform these tasks in SAS Enterprise Miner:
* Continue to use the same diagram. Define and create the data set CREDIT_SCORE for scoring. The variables (their roles and measurement levels) in the CREDIT_SCORE data should be set as identical to those in the CREDIT data. The only exception is that the scoring data does not have a TARGET variable.
* Find the best model out of Decision Tree, Decision Tree (3-way), Regression, and Neural Network as defined by each of the four model's overall performance in the validation data measured by average squared error. Now, use this best model to score the CREDIT_SCORE data.
CREDIT SCORE:
The median of the predicted probabilities of TARGET=1 in the scoring data is in which of the following ranges?
Response:
- A. less than 0.149999
- B. 0.85 or more
- C. 0.50-0.849999
- D. 0.15-0.499999
正解：A
質問 # 62
Assume you have two equally appealing logistic regression models. Then, if you have to select only one out of these two models, you should select the one that has which of the following?
Response:
- A. smaller value of gamma
- B. smaller value of SBC (Schwarz,s Bayesian criterion)
- C. all of the above
- D. higher value of AIC (Akaike,s information criterion)
正解：B
質問 # 63
For the variable TLCnt24, apply a Max Normal transformation. What transformation was selected by SAS Enterprise Miner?
Response:
- A. Square Root
- B. Square
- C. Log
- D. Exponential
正解：A
質問 # 64
Perform these tasks in SAS Enterprise Miner:
*Continue to use the same diagram. Define and create the data set CREDIT_SCORE for scoring. The variables (their roles and measurement levels) in the CREDIT_SCORE data should be set as identical to those in the CREDIT dat a. The only exception is that the scoring data does not have a TARGET variable.
* Find the best model out of Decision Tree, Decision Tree (3-way), Regression, and Neural Network as defined by each of the four model's overall performance in the validation data measured by average squared error. Now, use this best model to score the CREDIT_SCORE data.
CREDIT SCORE:
The percentage of TARGET=1 as predicted by the best model on the scoring data is in which of the following ranges?
Response:
- A. 7% or higher
- B. 6%-6.99%
- C. 5%-5.99%
- D. under 4.99%
正解：D
質問 # 65
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.
What percentage of all observations is being correctly predicted in the test data set by the decision tree?
Response:
- A. 16.8874%
- B. 85.2222%
- C. 83.1126%
- D. 84.5212%
正解：C
質問 # 66
Refer to the exhibit:
When the Explore button is selected in the graphic, what information will be displayed?
Select one:
Response:
- A. Sample Properties, Sample Statistics, sample data with all variables and values, and a histogram of the data for the selected variable
- B. Sample Properties, Sample Statistics for all variables, and sample data for all variables
- C. Sample Properties, Sample Statistics, sample data for the selected variable, and a pie chart of the selected variable
- D. Sample Properties, Sample Statistics, sample data for the selected variable, and a histogram of the data for the selected variable
正解：D
質問 # 67
Refer to the following profit matrix and confusion matrix for a campaign soliciting product purchases. The predicted variable is a binary outcome.
Based on the above tables, what is the average profit? You may use a calculator for this question. On the certification exam, an on-screen calculator is provided for you.
Select one:
Response:
- A. 0
- B. 6.9
- C. 1
- D. 86.25
正解：A
質問 # 68
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A00-255認証試験問題集の解答を提供しています：https://drive.google.com/open?id=1-Na-bQzW31OVuObjl-vWoLKDav7A_DF8
更新されたA00-255試験練習テスト問題：https://www.passtest.jp/SASInstitute/A00-255-shiken.html