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SAS Institute A00-405 Exam With Confidence Using Practice Dumps

Exam Code:
A00-405
Exam Name:
SAS Viya 3.5 Natural Language Processing and Computer Vision
Certification:
Natural Language and Computer Vision Specialist
Vendor:
SAS Institute
Questions:
60
Last Updated:
Apr 26, 2025
Exam Status:
Stable
SAS Institute A00-405

A00-405: Natural Language and Computer Vision Specialist Exam 2025 Study Guide Pdf and Test Engine

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Related SAS Institute Exams

SAS Viya 3.5 Natural Language Processing and Computer Vision Questions and Answers

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Question 1

Which feature is enabled in the default settings of the Text Parsing Node?

Options:

A.

misspelling detection

B.

synonym list

C.

minimum number of documents

D.

start list

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Question 2

CASL

Python

Review the code in the CASL and Python tabs The code sets are the same but in different languages Given this code which statement correctly describes this recurrent neural network built by the code set1?

Options:

A.

The RNN assigns a classification to each element in the sequence

B.

The RNN predicts the next value in a sequence

C.

The RNN assigns a classification to the entire sequence

D.

The RNN predicts the previous value in a sequence

Question 3

Which statement is TRUE regarding the impact of node placement"?

Options:

A.

When a Concept node appears after a Text Parsing node, only kept terms derived by the Text Parsing node can be used in concept creation

B.

When a Concept node is placed after a Text Parsing node: the term table is affected by the concepts defined in the Concept node and the concepts impact every node that follows the Concept node

C.

When the Concept node appears before the Text Parsing node in the pipeline, the spell-checking features of the Concept node produce parent and child terms that show up as synonyms in the term table created by the Text Parsing node

D.

When a Concept node is placed before a Text Parsing node the term table is affected by the concepts defined in the Concept node and the concepts impact every node that follows the Text Parsing node

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