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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Supervised Machine Learning Models | - Model selection techniques
|
| Topic 2: Machine Learning Pipelines in SAS Viya | - Pipeline construction
|
| Topic 3: Data Preparation for Machine Learning | - Feature engineering
|
| Topic 4: Model Evaluation and Deployment | - Model deployment
|
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
1. Which metric is commonly used to evaluate the performance of a regression model?
A) Precision
B) Mean Absolute Error (MAE)
C) F1 Score
D) Confusion Matrix
2. What is the primary objective of model evaluation in the context of building predictive models?
A) Assessing the model's performance and accuracy
B) Discovering patterns in data
C) Cleaning the data
D) Visualizing data
3. Which type of model is commonly used for anomaly detection in datasets?
A) Decision Trees
B) Principal Component Analysis (PCA)
C) Linear Regression
D) Clustering Models
4. Which type of data source typically stores structured data in a tabular format?
A) Text documents
B) APIs
C) Relational databases
D) NoSQL databases
5. What is overfitting in machine learning, and how can it be addressed in a pipeline?
A) Overfitting occurs when the model is too simple and underperforms.
B) Overfitting is not a concern in machine learning pipelines.
C) Overfitting occurs when the model fits the training data too closely and may not generalize well. It can be addressed by regularization techniques.
D) Overfitting occurs when the model is too complex and overperforms.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: A | Question # 3 Answer: D | Question # 4 Answer: C | Question # 5 Answer: C |
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