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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Data Preparation for Machine Learning | - Feature engineering
|
| Topic 2: Supervised Machine Learning Models | - Regression and classification models
|
| Topic 3: Machine Learning Pipelines in SAS Viya | - Model tuning and optimization
|
| Topic 4: Model Evaluation and Deployment | - Model assessment metrics
|
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
Question 1
Which of the following is a common technique for handling missing data in a machine learning pipeline?
A. Ignoring missing data
B. Replacing missing values with zeros
C. Imputing missing values
D. Deleting rows with missing data
Question 2
Which feature extraction method can take both interval variables and class variables as inputs?
A. Principal component analysis
B. Singular value decomposition
C. Robust PCA
D. Autoencoder
Question 3
Which evaluation metric is commonly used for assessing the performance of a binary classification model?
A. R-squared
B. Root Mean Squared Error (RMSE)
C. Mean Absolute Error (MAE)
D. Accuracy
Question 4
In a supervised machine learning pipeline, what is the purpose of the test data set?
A. To validate the model's performance
B. To train the machine learning model
C. To evaluate the model's predictions
D. To preprocess the data
Question 5
What is the purpose of data profiling in data source management?
A. To optimize data storage
B. To create data visualizations
C. To assess the quality and characteristics of data
D. To execute data queries
Solutions:
| Question 1 Answer: C | Question 2 Answer: D | Question 3 Answer: D | Question 4 Answer: A | Question 5 Answer: C |
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