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The A00-406 SAS Viya Supervised Machine Learning Pipelines exam is a key credential in the dynamic field of data science and analytics. Excelling in this exam can pave the way for a multitude of career prospects and reinforce your proficiency. This certification explores the theory behind supervised machine learning models in depth. It uses practical examples and exercises to strengthen the comprehension of these concepts and the analytical approach to tackling business problems.

A00-406 SAS Viya Supervised Machine Learning Pipelines

This exam is administered by SAS and Pearson VUE.

50-55 multiple choice and short-answer questions.

90 minutes to complete exam.

Passing score is 62%.

Certification expires after 5 years.

This exam is based on SAS Viya 4.0.

Cost: $180

Create a project in Model Studio 

Explore the data

Modify data

Use the VARIABLE SELECTION node to identify important variables to be included in a predictive model 

Describe key machine learning terms and concepts

Build models with decision trees and ensemble of trees 

Build models with neural networks

Build models with support vector machines

Use Model Interpretability tools to explain black box models

Incorporate externally written code

Explain the principles of Model Assessment 

Assess and compare models in Model Studio

Deploy a model

1. Which of the following is an example of a NoSQL database that is commonly used to store unstructured data?

A. MySQL

B. MongoDB

C. Oracle Database

D. Microsoft SQL Server

Answer: B

2. What is the primary goal of A/B testing in the context of model deployment?

A. To evaluate the model's accuracy

B. To compare two different versions of a model or strategy to determine which performs better

C. To assess data quality

D. To create synthetic data

Answer: B

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

A. A model's inability to generalize to new data

B. Systematic errors that cause a model to consistently underpredict or overpredict

C. The simplicity of a model

D. The overall accuracy of a model

Answer: B

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

A. It represents the trade-off between underfitting and overfitting.

B. It indicates the trade-off between accuracy and precision.

C. It refers to the trade-off between the number of features and the model's complexity.

D. It is not relevant in machine learning.

Answer: A

5. What is the purpose of cross-validation in model building and evaluation?

A. Splitting the dataset into training and testing sets

B. Reducing the dataset size

C. Assessing the model's generalization performance

D. Generating synthetic data

Answer: C

6. What is the purpose of a "canary release" in the context of model deployment?

A. To assess data quality

B. To deploy a new model version to a small subset of users or systems for testing

C. To create synthetic data

D. To evaluate model accuracy

Answer: B

7. Which algorithm is commonly used for decision-making tasks in classification models?

A. K-Means

B. Decision Trees

C. Principal Component Analysis (PCA)

D. Linear Regression

Answer: B

8. Which of the following best describes unstructured data?

A. Data that is organized in rows and columns

B. Data that is difficult to process and lacks a predefined structure

C. Data stored in a relational database

D. Data with a clear schema

Answer: B

9. What is the main advantage of using a RESTful API (Representational State Transfer) as a data source?

A. Real-time data processing

B. Support for complex data structures

C. Simple and standardized communication

D. High security features

Answer: C

10. Which type of model is commonly used for anomaly detection in datasets?

A. Decision Trees

B. Clustering Models

C. Linear Regression

D. Principal Component Analysis (PCA)

Answer: B