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Exam Code: A00-225
Exam Name: SAS Advanced Predictive Modeling
Exam Q&As: 60 Q&As
Last update: May 25, 2024

Product Description

Acing the SAS Institute A00-225 Exam with QuestionsTube

The SAS Certified Specialist: Advanced Predictive Modeling certification is a great SAS certified credential for analysts who are creating advanced predictive models using big data, which indicates the ability to:

  1. Deploying open source models in SAS
  2. Machine learning and predictive modeling techniques.
  3. Application of machine learning and predictive modeling techniques to big, distributed and in-memory data sets.

To gain this certification, a passing score is needed on the A00-225 exam, which has 50-55 multiple-choice, short-answer, and interactive questions and takes 110 minutes to complete. Candidates must achieve a score of 67% correct to pass.

Read the SAS Advanced Predictive Modeling A00-225 Exam Content

  • 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
  • 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
  • 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
  • Incorporate an existing R program into SAS Enterprise Miner
  • Incorporate an existing Python program into SAS Enterprise Miner

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