SASInstitute - A00-255–Trustable Certification Torrent

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SASInstitute A00-255 Certification Exam is designed for professionals who want to demonstrate their proficiency in predictive modeling using SAS Enterprise Miner. SAS Predictive Modeling Using SAS Enterprise Miner 14 certification is especially useful for data analysts, statisticians, and business analysts who work with large datasets and want to use predictive models to make informed business decisions.

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The SAS Institute, the creator of SAS Enterprise Miner, is a leading provider of analytics software and solutions. Their certification program is highly respected in the industry, as it ensures that certified professionals have the skills and knowledge needed to use SAS software effectively. The A00-255 Exam is a rigorous test of the candidate's ability to use SAS Enterprise Miner to solve real-world problems, and passing it demonstrates a high level of expertise in predictive modeling. Certification can help professionals advance their careers and stand out in the competitive job market.

SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions (Q23-Q28):

NEW QUESTION # 23
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. 0-1.24999
C. 1.75 or more
D. 1.25-.49999

Answer: A

NEW QUESTION # 24
How many hidden layers are generally needed in an MLP-based neural network to capture a discontinuous relationship between inputs and target?
Response:

A. three or more hidden layers
B. one hidden layer
C. two hidden layers
D. no hidden layer, direct connection between inputs and output is preferred

Answer: C

NEW QUESTION # 25
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

Answer: A

NEW QUESTION # 26
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. 0.15-0.499999
B. 0.50-0.849999
C. less than 0.149999
D. 0.85 or more

Answer: C

NEW QUESTION # 27
For the Variable Selection node, which statement describes the R-squared variable selection criterion?
Select one:
Response:

A. It uses a chi-squared Decision Tree with no Bonferoni adjustment to select the relevant inputs.
B. It is similar to a decision tree algorithm in being able to detect nonlinear and non-additive relationships between inputs and the target.
C. It uses a squared correlation and then a stepwise regression to eliminate irrelevant inputs.
D. It looks for a set of colinear inputs that correlate with the target.

Answer: C

NEW QUESTION # 28
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