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SASInstitute SAS Certified Specialist A00-406

A00-406

Exam Code: A00-406

Exam Name: SAS® Viya® Supervised Machine Learning Pipelines

Updated: Jul 31, 2026

Q & A: 98 Questions and Answers

A00-406 Free Demo download:

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SASInstitute A00-406 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Model Assessment and Deployment24-30%- Select champion models
- Deploy models into production
- Evaluate and compare model performance
- Apply model assessment principles
Topic 2: Data Sources30-36%- Modify and prepare data
- Explore and understand data
- Reduce dimensionality
- Create a project in Model Studio
- Perform variable selection
Topic 3: Building Models40-46%- Build neural networks and SVM models
- Build decision trees and ensemble models
- Use model interpretability tools
- Build regression models
- Understand supervised machine learning concepts
- Integrate custom code

SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:

1. When building a deep learning neural network, what is the purpose of the activation function in each neuron?

A) To control the number of hidden layers
B) To initialize the model
C) To define the learning rate
D) To introduce non-linearity


2. What is the primary objective of model validation during the model assessment phase?

A) To ensure the model generalizes well to new, unseen data
B) To assess the accuracy of the model
C) To build a model from scratch
D) To create synthetic data


3. Which technique is used for feature selection in a machine learning pipeline when dealing with a large number of features?

A) Regularization
B) One-Hot Encoding
C) Principal Component Analysis (PCA)
D) Naive Bayes


4. What is the purpose of an ROC curve (Receiver Operating Characteristic) in model assessment?

A) To visualize data distribution
B) To measure feature importance
C) To evaluate regression models
D) To compare a model's true positive rate with the false positive rate


5. Which type of model is commonly used for anomaly detection in datasets?

A) Clustering Models
B) Linear Regression
C) Decision Trees
D) Principal Component Analysis (PCA)


Solutions:

Question # 1
Answer: D
Question # 2
Answer: A
Question # 3
Answer: A
Question # 4
Answer: D
Question # 5
Answer: A

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