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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 |
| Available Languages: | English |
| Exam Format: | Scenario-based Questions, Multiple Choice |
| Exam Duration: | 110 minutes |
| Exam Price: | USD 180 |
| Related Certifications: | SAS Certified Specialist: Machine Learning Using SAS Viya |
| Certificate Validity Period: | 3 years |
| 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 |
|---|---|
| Supervised Machine Learning Models | - Model selection techniques
|
| Machine Learning Pipelines in SAS Viya | - Model tuning and optimization
|
| Model Evaluation and Deployment | - Model deployment
|
| Data Preparation for Machine Learning | - Feature engineering
|
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
Question #1
What is the purpose of hyperparameter tuning in a machine learning pipeline?
- A. To select the most important features
- B. To evaluate the model's predictions
- C. To train the model
- D. To optimize the model's hyperparameters for better performance
Correct Answer: D 🗳️
Question #2
When deploying a model, what is "model explainability"?
- A. The process of data preprocessing
- B. The capability to interpret and understand the model's decisions and predictions
- C. The time it takes to make predictions
- D. The simplicity of the model
Correct Answer: B 🗳️
Question #3
In model assessment, what does "cross-validation" aim to address?
- A. Training a model
- B. Model deployment
- C. Data preprocessing
- D. Overfitting and generalization
Correct Answer: D 🗳️
Question #4
What is the primary objective of model evaluation in the context of building predictive models?
- A. Cleaning the data
- B. Visualizing data
- C. Discovering patterns in data
- D. Assessing the model's performance and accuracy
Correct Answer: D 🗳️
Question #5
What is the primary purpose of model assessment in the context of data science and machine learning?
- A. Data visualization
- B. Evaluating and selecting the best-performing model
- C. Data preprocessing
- D. Model building
Correct Answer: B 🗳️
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By Edwina
