A00-406 study materials - SASInstitute A00-406 dumps VCE

SASInstitute A00-406 : SAS® Viya® Supervised Machine Learning Pipelines

Exam Code: A00-406

Exam Name: SAS® Viya® Supervised Machine Learning Pipelines

Updated: Aug 01, 2026

Q & A: 98 Questions and Answers

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SASInstitute A00-406 Exam Syllabus Topics:

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

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

1. What does the term "bagging" refer to in ensemble learning?

A) A technique that reduces model complexity
B) A type of feature extraction
C) The process of combining multiple identical models to reduce variance
D) A form of dimensionality reduction


2. What is the primary goal of A/B testing in the context of model deployment?

A) To assess data quality
B) To create synthetic data
C) To compare two different versions of a model or strategy to determine which performs better
D) To evaluate the model's accuracy


3. Refer to the exhibit below:

Based on the output from the Data Exploration node shown in the exhibit, which variable has the most thin tails (most platykurtic distribution)?

A) Logi_rfm12
B) Logi_rfm6
C) Logi_rfm8
D) Logi_rfm4


4. What is the purpose of data profiling in data source management?

A) To execute data queries
B) To create data visualizations
C) To assess the quality and characteristics of data
D) To optimize data storage


5. What is the primary objective of model evaluation in the context of building predictive models?

A) Assessing the model's performance and accuracy
B) Visualizing data
C) Cleaning the data
D) Discovering patterns in data


Solutions:

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

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