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CT-AI Engine Package

CT-AI Testing Engine (Downloadable)
Recommended For Exam Preparation
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Update date : 17-Jul-2024
QA: 40 Answers With In-Depth Explanation
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CT-AI PDF + Testing Engine Mega Pack
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Highly Recommended and Cover All Latest 2024 Topics in Syllabus.
Updated : 17-Jul-2024
QA : 40
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CT-AI PDF Package

CT-AI PDF Exam (Downloadable)
Latest 2024 Syllabus Topics Included
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Updated : 17-Jul-2024
QA : 40 Answers With In-Depth Explanation
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CT-AI Question and answers Include

Total Questions: 40 Q&A's
Single Choice: 40 Q&A's

ISTQB CT-AI Exam Dumps - Valid Questions Answers

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ISTQB CT-AI Last Week Results!

20

Customers Passed
ISTQB CT-AI

95%

Average Score In Real
Exam At Testing Centre

86%

Questions came word by
word from this dump

CT-AI Questions and Answers

Question # 1

Which ONE of the following activities is MOST relevant when addressing the scenario where you have more than the required amount of data available for the training?

SELECT ONE OPTION

A.

Feature selection

B.

Data sampling

C.

Data labeling

D.

Data augmentation

Question # 2

A system was developed for screening the X-rays of patients for potential malignancy detection (skin cancer). A workflow system has been developed to screen multiple cancers by using several individually trained ML models chained together in the workflow.

Testing the pipeline could involve multiple kind of tests (I - III):

I.Pairwise testing of combinations

II.Testing each individual model for accuracy

III.A/B testing of different sequences of models

Which ONE of the following options contains the kinds of tests that would be MOST APPROPRIATE to include in the strategy for optimal detection?

SELECT ONE OPTION

A.

Only III

B.

I and II

C.

I and III

D.

Only II

Question # 3

Which ONE of the following describes a situation of back-to-back testing the LEAST?

SELECT ONE OPTION

A.

Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.

B.

Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for same data

C.

Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.

D.

Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.

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