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iSQI CT-AI_(v1.0)_World Exam With Confidence Using Practice Dumps

Exam Code:
CT-AI_(v1.0)_World
Exam Name:
ISTQB Certified Tester AI Testing (v 1.0)
Certification:
Vendor:
Questions:
40
Last Updated:
Apr 27, 2025
Exam Status:
Stable
iSQI CT-AI_(v1.0)_World

CT-AI_(v1.0)_World: ISQI certification Exam 2025 Study Guide Pdf and Test Engine

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ISTQB Certified Tester AI Testing (v 1.0) Questions and Answers

Question 1

Max. Score: 2

Al-enabled medical devices are used nowadays for automating certain parts of the medical diagnostic processes. Since these are life-critical process the relevant authorities are considenng bringing about suitable certifications for these Al enabled medical devices. This certification may involve several facets of Al testing (I - V).

I.Autonomy

II.Maintainability

III.Safety

IV.Transparency

V.Side Effects

Which ONE of the following options contains the three MOST required aspects to be satisfied for the above scenario of certification of Al enabled medical devices?

SELECT ONE OPTION

Options:

A.

Aspects II, III and IV

B.

Aspects I, II, and III

C.

Aspects III, IV, and V

D.

Aspects I, IV, and V

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Question 2

Which ONE of the following options describes a scenario of A/B testing the LEAST?

SELECT ONE OPTION

Options:

A.

A comparison of two different websites for the same company to observe from a user acceptance perspective.

B.

A comparison of two different offers in a recommendation system to decide on the more effective offer for same users.

C.

A comparison of the performance of an ML system on two different input datasets.

D.

A comparison of the performance of two different ML implementations on the same input data.

Question 3

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

SELECT ONE OPTION

Options:

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.