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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Model Assessment and Deployment | 24-30% | - Assessing model performance (metrics, ROC curves, confusion matrices) - Deploying models into production |
| Topic 2: Data Sources | 30-36% | - Exploring and modifying data - Importing and preparing data - Dimensionality reduction and feature engineering |
| Topic 3: Building Models | 40-46% | - Supervised model creation (decision trees, ensembles, SVM, neural networks) - Model comparison and selection |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
Which statements are true for the F1 score?
(Choose 2.)
- A. F1 score is applicable to a model with a binary target.
- B. F1 score is calculated based on a cut off value.
- C. F1 score is applicable to a model with an interval target.
- D. F1 score is calculated based on a depth value.
Correct Answer: A,B 🗳️
Which algorithm is commonly used for decision-making tasks in classification models?
- A. Decision Trees
- B. Principal Component Analysis (PCA)
- C. K-Means
- D. Linear Regression
Correct Answer: A 🗳️
What is the main advantage of ensemble learning methods, such as Random Forest, in a machine learning pipeline?
- A. They require minimal data preprocessing.
- B. They combine multiple models to improve predictive performance.
- C. They are not suitable for large datasets.
- D. They are simple and easy to interpret.
Correct Answer: B 🗳️
What is the primary purpose of model assessment in the context of data science and machine learning?
- A. Evaluating and selecting the best-performing model
- B. Model building
- C. Data preprocessing
- D. Data visualization
Correct Answer: A 🗳️
Which of the following is an example of a NoSQL database that is commonly used to store unstructured data?
- A. MySQL
- B. Microsoft SQL Server
- C. MongoDB
- D. Oracle Database
Correct Answer: C 🗳️
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