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SASInstitute A00-406 Exam Syllabus Topics:

SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:

1. Which technique is used for feature selection in a machine learning pipeline when dealing with a large number of features?

A) One-Hot Encoding
B) Naive Bayes
C) Regularization
D) Principal Component Analysis (PCA)

2. Which statements are true for the F1 score?
(Choose 2.)

A) F1 score is calculated based on a cut off value.
B) F1 score is applicable to a model with an interval target.
C) F1 score is calculated based on a depth value.
D) F1 score is applicable to a model with a binary target.

3. When building a recommendation system, what does "collaborative filtering" rely on?

A) The characteristics of the items being recommended
B) The past behavior or preferences of users
C) Item-based clustering
D) The popularity of items

4. When deploying a model, what is "model explainability"?

A) The time it takes to make predictions
B) The simplicity of the model
C) The capability to interpret and understand the model's decisions and predictions
D) The process of data preprocessing

5. What is the significance of the "bias-variance trade-off" in machine learning?

A) It indicates the trade-off between accuracy and precision.
B) It is not relevant in machine learning.
C) It represents the trade-off between underfitting and overfitting.
D) It refers to the trade-off between the number of features and the model's complexity.

Solutions: