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What is SAS Certified Advanced Analytics Professional Using SAS 9 Certification?

  • Writer: Palak Mazumdar
    Palak Mazumdar
  • Aug 23, 2018
  • 4 min read

SAS Certified Advanced Analytics Professional Using SAS 9 certification questions and exam summary helps you to get focused on the exam. This guide also helps you to be on A00-225 exam track to get certified with good score in the final exam

It is essential that the candidate has a firm understanding and mastery of the functionalities for predictive modeling available in SAS 9.4. Successful candidates should have the ability to:

  • SAS Enterprise Miner

  • SAS/STAT

  • SAS Visual Analytics

  • SAS LASR Analytic Server

A00-225 - SAS Certified Advanced Analytics Professional Using SAS 9 Certification Summary

  • Exam Name: SAS Advanced Predictive Modeling

  • Exam Code: A00-225

  • Exam Duration: 110 minutes

  • Exam Questions: 50 to 55 Multiple choices or short answer questions

  • Passing Score: 67%

  • Exam Price: $180 (USD)

  • Training:


A00-225 SAS Certified Advanced Analytics Professional Using SAS 9 Certification Questions:


Q 1:What is the maximum number of response variables that SAS Visual Statistics allows for a decision tree?

Options:

A: 2

B: 4

C: 1

D: 3


Q 2: When mean imputation is performed on data after the data is partitioned for honest assessment, what is the most appropriate method for handling the mean imputation?

Options:

A: The sample means from the training data set are applied to the validation and test data sets.

B: The sample means from the validation data set are applied to the training and test data sets.

C: The sample means from each partition of the data are applied to their own partition.

D: The sample means from the test data set are applied to the training and validation data sets.


Q 3: What is a linear Perception?

Options:

A: A linear Perceptron is a non-parametric model.

B: A linear Perceptron is a nonlinear model.

C: A linear Perceptron is a general linear model.

D: A linear Perceptron is a generalized linear model.


Q 4: A predictive model uses a data set that has several variables with missing values. What two problems can arise with this model? (Choose two.)

Options:

A: The model will likely be overfit.

B: There will be a high rate of collinearity among input variables.

C: New cases with missing values on input variables cannot be scored without extra data processing.

D: Fewer observations will be used in the model building process.


Q 5: Consider a Generalized Additive Neural Network (GANN) with 3 continuous inputs and 2 hidden nodes for each input. How many parameters do you need to estimate when training the neural network?

Options:

A: 19

B: 21

C: 25

D: 22


Q 6: Refer to the fit summary from SAS Visual Statistics in the exhibit below.

What can be concluded from the fit summary?

Options:

A: Average Sales is a significant predictor when Customer Value Level = E.

B: Customer Value Level C has no important variables associated with it.

C: Average Sales is an important predictor when Customer Value Level = C.

D: Customer Value Level is not a significant predictor in this model.


Answers:

Question: 1 Answer: C

Question: 2 Answer: A

Question: 3 Answer: D

Question: 4 Answer: C, D

Question: 5 Answer: D

Question: 6 Answer: A


SAS Certified Advanced Analytics Professional Using SAS 9 Certification A00-225 Exam Syllabus:


1.Neural Networks (20%)

  • Describe key concepts underlying neural networks.

  • Use two architectures offered by the Neural Network node to model either linear or non-linear input-output relationships.

  • Use optimization methods offered by the SAS Enterprise Miner Neural Network node to efficiently search the parameter space in a neural network.

  • Construct custom network architectures by using the NEURAL procedure (PROC Neural).

  • Based upon statistical considerations, use either time delayed neural networks, surrogate models to augment neural networks.

  • Use the HP Neural Node to perform high-speed training of a neural network.

2. Logistic Regression (30%)

  • Score new data sets using the LOGISTIC and PLM procedures.

  • Identify the potential challenges when preparing input data for a model.

  • Use the DATA step to manipulate data with loops, arrays, conditional statements and functions.

  • Improve the predictive power of categorical inputs.

  • Screen variables for irrelevance and non-linear association using the CORR procedure.

  • Screen variables for non-linearity using empirical logit plots.

  • Apply the principles of honest assessment to model performance measurement.

  • Assess classifier performance using the confusion matrix.

  • Model selection and validation using training and validation data.

  • Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection.

  • Establish effective decision cut-off values for scoring.

3. Predictive Analytics on Big Data (40%)

  • Build and interpret a cluster analysis in SAS Visual Statistics.

  • Explain SAS high-performance computing.

  • Perform principal component analysis.

  • Analyze categorical targets using logistic regression in SAS Visual Statistics.

  • Analyze categorical targets using decision trees in SAS Visual Statistics.

  • Analyze categorical targets using decision trees in PROC IMSTAT.

  • Analyze categorical targets using logistic regression in PROC IMSTAT.

  • Build random forest models with PROC IMSTAT.

  • Analyze interval targets with SAS Visual Statistics.

  • Analyze interval targets with PROC IMSTAT.

  • Analyze zero inflated models with HPGLM in Enterprise Miner.

4. Open Source Models in SAS (10%)

  • Incorporate an existing R program into SAS Enterprise Miner.

  • Incorporate an existing Python program into SAS Enterprise Miner.


How to Register for SAS Certified Advanced Analytics Professional Using SAS 9 Certification Exam?


Exam Registration:

New User (Has never taken a SAS exam before):

  • First-time users must create a new web account within Pearson VUE before registering for a SAS exam.

  • It can take up to two business days to receive your user name and password, which you will need for exam registration.

Returning Candidate (Has taken or registered to take a SAS exam(s) at Pearson VUE before BUT has never logged into SAS Certification Manager):

  • If you already have a Pearson VUE account but have forgotten your sign-in information, follow the links on the Pearson VUE site to retrieve this information.

Returning Candidate (Has taken SAS exam(s) and has logged in to SAS Certification Manager before):

  • Exam registrations must be completed at least 24 hours in advance and cannot be completed at the test facility.

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