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SASInstitute A00-406 Exam Syllabus Topics:

SectionWeightObjectives
Model Assessment and Deployment24-30%- Assessing model performance (metrics, ROC curves, confusion matrices)
- Deploying models into production
Building Models40-46%- Model comparison and selection
- Supervised model creation (decision trees, ensembles, SVM, neural networks)
Data Sources30-36%- Importing and preparing data
- Dimensionality reduction and feature engineering
- Exploring and modifying data

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

Question 1

What is the primary function of a data catalog in managing data sources?

A. Data storage
B. Data visualization
C. Data analysis
D. Data documentation and discovery


Question 2

What does the term "bias" in machine learning refer to?

A. Systematic errors that cause a model to consistently underpredict or overpredict
B. The simplicity of a model
C. A model's inability to generalize to new data
D. The overall accuracy of a model


Question 3

When building a deep learning neural network, what is the purpose of the activation function in each neuron?

A. To introduce non-linearity
B. To define the learning rate
C. To initialize the model
D. To control the number of hidden layers


Question 4

Which of the following best describes unstructured data?

A. Data stored in a relational database
B. Data that is difficult to process and lacks a predefined structure
C. Data that is organized in rows and columns
D. Data with a clear schema


Question 5

What is the purpose of cross-entropy loss in machine learning, especially in the context of classification?

A. To measure the dissimilarity between predicted and actual class probabilities
B. To quantify the variance of a model
C. To calculate the mean squared error of a regression model
D. To evaluate feature importance


Solutions:

Question 1
Answer: D
Question 2
Answer: A
Question 3
Answer: A
Question 4
Answer: B
Question 5
Answer: A

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