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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Supervised Machine Learning Models | - Regression and classification models
|
| Data Preparation for Machine Learning | - Feature engineering
|
| Model Evaluation and Deployment | - Model assessment metrics
|
| Machine Learning Pipelines in SAS Viya | - Pipeline construction
|
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
Which of the following is NOT a common data format for exchanging data between systems?
- A. HTML (Hypertext Markup Language)
- B. JSON (JavaScript Object Notation)
- C. SQL (Structured Query Language)
- D. CSV (Comma-Separated Values)
Correct Answer: C 🗳️
A project has been created and a pipeline has been run in Model Studio.
Which project setting can you edit?
- A. Event-based Sampling proportions
- B. Rules for model comparison statistic
- C. Advisor Options for missing values
- D. Partition Data percentages
Correct Answer: B 🗳️
Which technique is commonly used for feature scaling or normalization in machine learning pipelines?
- A. Standardization
- B. Principal Component Analysis (PCA)
- C. One-Hot Encoding
- D. Decision Trees
Correct Answer: A 🗳️
What is feature engineering in the context of machine learning pipelines?
- A. Creating new features from existing data
- B. Building a machine learning model from scratch
- C. Applying the model to new data
- D. Testing the model's performance
Correct Answer: A 🗳️
Which hyperparameter in a decision tree model controls the depth of the tree and helps prevent overfitting?
- A. Max depth
- B. Min samples split
- C. Learning rate
- D. Max features
Correct Answer: A 🗳️
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