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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Machine Learning Pipelines in SAS Viya | - Model tuning and optimization
|
| Supervised Machine Learning Models | - Regression and classification models
|
| Model Evaluation and Deployment | - Model deployment
|
| Data Preparation for Machine Learning | - Feature engineering
|
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
1. Which metric is commonly used to evaluate the performance of a regression model?
A) F1 Score
B) Confusion Matrix
C) Precision
D) Mean Absolute Error (MAE)
2. When deploying a machine learning model, what is meant by "model latency"?
A) The time it takes for the model to make predictions once deployed
B) The time it takes to train a model
C) The time it takes to build a model
D) The time it takes to create synthetic data
3. What is the primary purpose of model deployment in the context of data science and machine learning?
A) Data preprocessing
B) Making the model available for use in real-world applications
C) Model evaluation
D) Model building
4. What is the purpose of a confusion matrix in the context of classification models?
A) To evaluate model performance, especially for binary classification
B) To visualize the data
C) To summarize the distribution of target variables
D) To compute the mean squared error
5. What is the purpose of cross-entropy loss in machine learning, especially in the context of classification?
A) To evaluate feature importance
B) To measure the dissimilarity between predicted and actual class probabilities
C) To calculate the mean squared error of a regression model
D) To quantify the variance of a model
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: A | Question # 3 Answer: B | Question # 4 Answer: A | Question # 5 Answer: B |
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