Prepare with SASInstitute A00-406 exam practice material, pass for sure
Updated: Sep 02, 2026
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SASInstitute A00-406 Exam Overview:
| Certification Vendor: | SAS Institute |
|---|---|
| Exam Name: | SAS® Viya® Supervised Machine Learning Pipelines |
| Exam Number: | A00-406 |
| Exam Format: | Multiple Choice, Scenario-based Questions |
| Exam Price: | USD 180 |
| Available Languages: | English |
| Certificate Validity Period: | 3 years |
| Related Certifications: | SAS Certified Specialist: Machine Learning Using SAS Viya |
| Exam Duration: | 110 minutes |
| Recommended Training: | SAS Machine Learning Using SAS Viya Training |
| Exam Registration: | SAS Certification Portal Registration |
| Sample Questions: | SASInstitute A00-406 Sample Questions |
| Exam Way: | Online proctored exam via Pearson VUE or authorized testing centers |
| Pre Condition: | No strict prerequisites; recommended familiarity with basic statistics, machine learning concepts, and SAS Viya platform |
| Official Syllabus URL: | https://www.sas.com/en_us/certification.html |
SASInstitute A00-406 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Model Evaluation and Deployment | - Model assessment metrics
|
| Topic 2: Machine Learning Pipelines in SAS Viya | - Pipeline construction
|
| Topic 3: Supervised Machine Learning Models | - Regression and classification models
|
| Topic 4: Data Preparation for Machine Learning | - Feature engineering
|
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
Question 1
When deploying a model, what is "model explainability"?
A. The simplicity of the model
B. The process of data preprocessing
C. The time it takes to make predictions
D. The capability to interpret and understand the model's decisions and predictions
Question 2
When deploying a machine learning model, what is "model drift"?
A. The process of feature extraction
B. A measure of feature importance
C. A sudden increase in the model's accuracy
D. A change in the distribution of the input data or target variable over time
Question 3
When building a deep learning neural network, what is the purpose of the activation function in each neuron?
A. To define the learning rate
B. To introduce non-linearity
C. To initialize the model
D. To control the number of hidden layers
Question 4
What is the main advantage of ensemble learning methods, such as Random Forest, in a machine learning pipeline?
A. They are simple and easy to interpret.
B. They combine multiple models to improve predictive performance.
C. They require minimal data preprocessing.
D. They are not suitable for large datasets.
Question 5
Which hyperparameter in a decision tree model controls the depth of the tree and helps prevent overfitting?
A. Max features
B. Learning rate
C. Max depth
D. Min samples split
Solutions:
| Question 1 Answer: D | Question 2 Answer: D | Question 3 Answer: B | Question 4 Answer: B | Question 5 Answer: C |
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