A00-406 Exam Preparation Material | SAS Viya Supervised Machine Learning Pipelines

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Prepare for the A00-406 SAS Viya Supervised Machine Learning Pipelines exam with CertQueen's independently developed study resources. Review important concepts, practice scenario-based questions, and use clear explanations to identify areas that require further study.

Question#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

Question#2

In the context of data sources, what is meant by data versioning?

A. Storing multiple copies of the same data to increase redundancy
B. Keeping track of different versions or changes to data over time
C. Encrypting data to protect against unauthorized access
D. Compressing data to reduce storage space

Question#3

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

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

Question#4

What is a data lake architecture designed to store primarily?

A. Highly structured data in tabular format
B. Data from a single source or department
C. All types of data, including structured and unstructured data
D. Only unstructured data in raw form

Question#5

What is the purpose of an ROC curve (Receiver Operating Characteristic) in model assessment?

A. To evaluate regression models
B. To visualize data distribution
C. To compare a model's true positive rate with the false positive rate
D. To measure feature importance

Exam Code: A00-406
Q & A: 97 Q&As         Updated:  Oct 07,2026

 

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Review Key Concepts

Review the technologies, products, processes, and practical skills covered by the current A00-406 exam objectives.

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Work through independently developed questions designed to strengthen your understanding of technical scenarios and decision-making.

Identify Knowledge Gaps

Use your results and the provided explanations to find weaker areas and focus your study more effectively.

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Close Knowledge Gaps

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