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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Model Assessment and Deployment | 24-30% | - Deploying models into production - Assessing model performance (metrics, ROC curves, confusion matrices) |
| Data Sources | 30-36% | - Exploring and modifying data - Importing and preparing data - Dimensionality reduction and feature engineering |
| Building Models | 40-46% | - Model comparison and selection - Supervised model creation (decision trees, ensembles, SVM, neural networks) |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
1. What is the primary goal of hyperparameter tuning during model building?
A) To improve data preprocessing techniques
B) To add more features to the model
C) To optimize the settings that control the model's learning process
D) To increase model complexity
2. Which statement is true regarding decision trees and models based on ensembles of trees?
A) A single decision tree will always be outperformed by a model based on an ensemble of trees.
B) In the Forest algorithm, each individual tree is pruned based on using minimum Average Squared Error.
C) In the gradient boosting algorithm, for all but the first iteration, the target is the residual from the previous decision tree model.
D) For a Forest model, the out-of-bag sample is simply the original validation data set from when the raw data partitioning took place.
3. Which algorithm is commonly used for binary classification in machine learning pipelines, especially when dealing with imbalanced datasets?
A) K-Means Clustering
B) Support Vector Machine (SVM)
C) Principal Component Analysis (PCA)
D) Linear Regression
4. What is the purpose of cross-entropy loss in machine learning, especially in the context of classification?
A) To quantify the variance of a model
B) To calculate the mean squared error of a regression model
C) To measure the dissimilarity between predicted and actual class probabilities
D) To evaluate feature importance
5. What is a data lake architecture designed to store primarily?
A) Only unstructured data in raw form
B) Highly structured data in tabular format
C) Data from a single source or department
D) All types of data, including structured and unstructured data
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: C | Question # 3 Answer: B | Question # 4 Answer: C | Question # 5 Answer: D |
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