SAS A00-402 Certification Exam Syllabus

A00-402 Syllabus, A00-402 PDF Download, SAS A00-402 Dumps, SAS Machine Learning Dumps PDF Download, SAS Certified Specialist - Machine Learning Using SAS Viya 3.5 PDF DownloadThis page is a one-stop solution for any information you may require for SAS Certified Specialist - Machine Learning Using SAS Viya 3.5 (A00-402) Certification exam. The SAS A00-402 Exam Summary, Syllabus Topics and Sample Questions provide the base for the actual SAS Machine Learning Specialist exam preparation, we have designed these resources to help you get ready to take your dream exam.

The SAS Certified Specialist - Machine Learning Using SAS Viya 3.5 credential is globally recognized for validating SAS Machine Learning knowledge. With the SAS Machine Learning Specialist Certification credential, you stand out in a crowd and prove that you have the SAS Machine Learning knowledge to make a difference within your organization. The SAS Certified Specialist - Machine Learning Using SAS Viya 3.5 Certification (A00-402) exam will test the candidate's knowledge on following areas.

SAS A00-402 Exam Summary:

Exam Name SAS Certified Specialist - Machine Learning Using SAS Viya 3.5
Exam Code A00-402
Exam Duration 100 minutes
Exam Questions 50-55
Passing Score 65%
Exam Price $180 (USD)
Training Machine Learning Using SAS Viya
Books Machine Learning with SAS® Viya
Exam Registration Pearson VUE
Sample Questions SAS Machine Learning Certification Sample Question
Practice Exam SAS Machine Learning Certification Practice Exam

SAS A00-402 Exam Topics:

Objective Details

Data Sources (30%)

Create a project in Model Studio - Bring data into Model Studio for analysis
  • Import data from a local source (Import tab)
  • Add data from a stored data source (Data Sources tab)
  • Use an in-memory data source (Available tab)

- Create Model Studio Pipelines with the New Pipeline window

  • Automatically generate pipelines
  • Pipeline templates

- Advanced Advisor options

  • Maximum class level
  • Maximum % missing
  • Interval cut-off

- Partition data into training, validation, and test

  • Explain why partitioning is important
  • Explain the different methods to partition data (stratified vs simple random)

- Use Event Based Sampling to oversample for rare events.

Explore the data - Use the DATA EXPLORATION node
- Profile data during data definition
- Preliminary data exploration using the data tab
- Save data with the SAVE DATA node
Modify data - Modify metadata with the MANAGE VARIABLES node
- Use the REPLACEMENT node to update variable values
- Use the TRANSFORMATION node to correct problems with input data sources, such as variables distribution or outliers
- Use the IMPUTE node to impute missing values and create missing value indicators
- Modify data within the DATA tab
Reduce the dimensionality of the data - Use the FEATURE EXTRACTION node
- Prepare text data for modeling with the TEXT MINING node
Use the VARIABLE SELECTION node to identify important variables to be included in a predictive model - Unsupervised Selection
- Fast Supervised Selection
- Linear Regression Selection
- Decision Tree Selection
- Forest Selection
- Gradient Boosting Selection
- Create Validation from Training
- Use multiple methods within the same VARIABLE SELECTION node.

Building Models (50%)

Describe key supervised machine learning terms and concepts - Data partitioning: training, validation, test data sets
- Observations (cases), independent (input) variables/features, dependent (target) variables
- Measurement scales: Interval, ordinal, nominal (categorical), binary variables
- Prediction types: decisions, rankings, estimates
- Dimensionality, redundancy, irrelevancy
- Decision trees, neural networks, regression models
- Model optimization, overfitting, underfitting, model selection
- Describe ensemble models
Build models with decision trees and ensemble of trees - Explain how decision trees identify split points
  • Split search algorithm
  • Recursive partitioning
  • Decision tree algorithms
  • Multiway vs. binary splits
  • Impurity reduction
  • Gini, entropy, Bonferroni, IRG, FTEST, variance
  • Compare methods to grow decision trees for categorical vs continuous response variables

- Explain the effect of missing values on decision trees
- Explain surrogate rules
- Explain the purpose of pruning decision trees
- Explain bagging vs. boosting methods
- Build models with the DECISION TREE node

  • Adjust splitting options
  • Adjust pruning options

- Build models with the GRADIENT BOOSTING node

  • Adjust general options: number of trees, learning rate, L1/L2 regularization rate
  • Adjust Tree Splitting options
  • Adjust early stopping
  • Adjust autotuning

- Build models with the FOREST node

  • Adjust number of trees
  • Adjust tree splitting options
  • Adjust autotuning

- Interpret decision tree, gradient boosting, and forest results (fit statistics, output, tree diagrams, tree maps, variable importance, error plots, autotuned results)

Build models with neural networks - Describe the characteristics of neural network models
  • Adaptive learning
  • Universal approximation
  • Neurons, hidden layers, perceptrons, multilayer perceptrons
  • Weights and bias
  • Activation functions
  • Optimization Methods (LBFGS and Stochastic Gradient Descent)
  • Variable standardization

- Build models with the NEURAL NETWORK node

  • Adjust number of layers and neurons
  • Adjust optimization options and early stopping criterion

- Interpret NEURAL NETWORK node results (network diagram, iteration plots, and output)

Build models with support vector machines - Describe the characteristics of support vector machines.
- Build model with the SVM node
  • Adjust general properties (Kernel, Penalty, Tolerance)
  • Perform Autotuning

- Interpret SVM node results (Output)

Use Model Interpretability tools to explain black box models - Partial Dependence plots
- Individual Conditional Expectation plots
- Local Interpretable Model-Agnostic Explanations plots
- Kernel-SHAP plots
Incorporate externally written code - Open Source Code node
- SAS Code node
- Score Code Import node

Model Assessment and Deployment (20%)

Explain the principles of Model Assessment

- Explain different dimensions for model comparison

  • Training speed
  • Model application speed
  • Tolerance
  • Model clarity

- Explain honest assessment

  • Evaluate a model with a holdout data set

- Use the appropriate fit statistic for different prediction types

  • Average error for estimates
  • Misclassification for decisions
Assess and compare models in Model Studio - Compare models with the MODEL COMPARISON node
- Compare models with the PIPELINE COMPARISON tab
- Interpret Fit Statistics, Lift Reports, ROC reports.
Deploy a model - Exporting score code
- Registering a model
- Publish a model

The SAS has created this credential to assess the knowledge and understanding of a candidate in the area as above via the certification exam. The SAS Machine Learning (A00-402) Certification exam contains a high value in the market being the brand value of the SAS attached with it. It is highly recommended to a candidate to do a thorough study and also get a hand full of the practice to clear SAS Certified Specialist - Machine Learning Using SAS Viya 3.5 exam without any hiccups.

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