CT-AI덤프 & CT-AI시험대비최신버전문제

참고: Itexamdump에서 Google Drive로 공유하는 무료, 최신 CT-AI 시험 문제집이 있습니다: https://drive.google.com/open?id=1feaK0sv1A-SSQKnAheZleniyML8c0a1X

ISTQB 인증 CT-AI시험에 도전해보려고 결정하셨다면 Itexamdump덤프공부가이드를추천해드립니다. Itexamdump덤프는 고객님께서 필요한것이 무엇인지 너무나도 잘 알고 있답니다. Itexamdump의 ISTQB 인증 CT-AI덤프는ISTQB 인증 CT-AI시험을 쉽게 만듭니다.

ISTQB CT-AI 시험요강:

주제	소개
주제 1	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
주제 2	systems from those required for conventional systems.
주제 3	Introduction to AI: This exam section covers topics such as the AI effect and how it influences the definition of AI. It covers how to distinguish between narrow AI, general AI, and super AI; moreover, the topics covered include describing how standards apply to AI-based systems.
주제 4	ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.
주제 5	Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.
주제 6	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
주제 7	Machine Learning ML: This section includes the classification and regression as part of supervised learning, explaining the factors involved in the selection of ML algorithms, and demonstrating underfitting and overfitting.

>> CT-AI덤프 <<

CT-AI시험대비 최신버전 문제 - CT-AI퍼펙트 덤프데모 다운로드

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최신 ISTQB AI Testing CT-AI 무료샘플문제 (Q146-Q151):

질문 # 146
An image classification system is being trained for classifying faces of humans. The distribution of the data is 70% ethnicity A and 30% for ethnicities B, C and D. Based ONLY on the above information, which of the following options BEST describes the situation of this image classification system?

A. This is an example of expert system bias.
B. This is an example of sample bias.
C. This is an example of algorithmic bias.
D. This is an example of hyperparameter bias.

정답：B

설명：
Sample bias occurs when the training data is not representative of the overall population that the model will encounter in practice.
In this case, the over-representation of ethnicity A (70%) compared to B, C, and D (30%) creates a sample bias, as the model may become biased towards better performance on ethnicity A.

질문 # 147
Which assignment of AI techniques to testing support is BEST?

A. Fuzzy logic for the generation of test cases
B. Probabilistic methods for the prediction of system failures
C. Classification for the optimization of regression test cases
D. Computational optimization techniques for defect prediction

정답：B

설명：
The ISTQB CT-AI syllabus (Section5.2 - AI for Testing) explains that various AI approaches can support testing activities. Probabilistic methods--one of the three major AI technique groups--are used topredict system failures, especially when dealing with uncertainty, likelihood estimation, and reliability analysis. This aligns precisely with Option B.

질문 # 148
"AllerEgo" is a product that uses sell-learning to predict the behavior of a pilot under combat situation for a variety of terrains and enemy aircraft formations. Post training the model was exposed to the real- world data and the model was found to be behaving poorly. A lot of data quality tests had been performed on the data to bring it into a shape fit for training and testing.
Which ONE of the following options is least likely to describes the possible reason for the fall in the performance, especially when considering the self-learning nature of the Al system?
SELECT ONE OPTION
* The difficulty of defining criteria for improvement before the model can be accepted.
* The fast pace of change did not allow sufficient time for testing.
* The unknown nature and insufficient specification of the operating environment might have caused the poor performance.
* There was an algorithmic bias in the Al system.

정답：

설명：
* A. The difficulty of defining criteria for improvement before the model can be accepted.
* Defining criteria for improvement is a challenge in the acceptance of AI models, but it is not directly related to the performance drop in real-world scenarios. It relates more to the evaluation and deployment phase rather than affecting the model's real-time performance post-deployment.
* B. The fast pace of change did not allow sufficient time for testing.
* This can significantly affect the model's performance. If the system is self-learning, it needs to adapt quickly, and insufficient testing time can lead to incomplete learning and poor performance.
* C. The unknown nature and insufficient specification of the operating environment might have caused the poor performance.
* This is highly likely to affect performance. Self-learning AI systems require detailed specifications of the operating environment to adapt and learn effectively. If the environment is insufficiently specified, the model may fail to perform accurately in real-world scenarios.
* D. There was an algorithmic bias in the AI system.
* Algorithmic bias can significantly impact the performance of AI systems. If the model has biases, it will not perform well across different scenarios and data distributions.
Given the context of the self-learning nature and the need for real-time adaptability, optionAis least likely to describe the fall in performance because it deals with acceptance criteria rather than real-time performance issues.

질문 # 149
Which of the following statements about bias in AI based systems is MOST correct?

A. Inappropriate bias is caused by data used for training not being representative of the real world
B. Inappropriate bias only affects ML systems that process data about people
C. Inappropriate bias can be caused by aspects of the algorithm or the data
D. Inappropriate bias is caused by overweighting of particular classes in algorithms

정답：C

설명：
The correct answer is D . The CT-AI syllabus explains that ML systems make decisions and predictions using algorithms and collected data , and that both can introduce bias into the results. Algorithmic bias can occur when the learning algorithm is incorrectly configured, for example where it overvalues some data compared with other data. Sample bias can occur when training data is not fully representative of the data space to which ML is applied. The syllabus further states that inappropriate bias is often caused by sample bias, but can also be caused by algorithmic bias.
Option A is too narrow because it describes only one algorithmic mechanism. Option B is also incomplete because it describes sample bias only. Option C is incorrect because the syllabus states that bias can be introduced into many types of AI-based systems, not only ML systems involving personal data. Therefore, the most complete and syllabus-aligned statement is that inappropriate bias can arise from either the algorithm or the data.
References/topics: CT-AI Syllabus Chapter 2, Section 2.4 "Bias."
=========

질문 # 150
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?

A. Aspects I, IV, and V
B. Aspects II, III and IV
C. Aspects III, IV, and V
D. Aspects I, II, and III

정답：C

설명：
For AI-enabled medical devices, the most required aspects for certification are safety, transparency, and side effects.
Safety (Aspect III): Critical for ensuring that the AI system does not cause harm to patients.
Transparency (Aspect IV): Important for understanding and verifying the decisions made by the AI system.
Side Effects (Aspect V): Necessary to identify and mitigate any unintended consequences of the AI system.

질문 # 151
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ISTQB CT-AI 덤프가 고객님의 기대를 가득 채워드릴수 있도록 정말로 노력하고 있는 Itexamdump랍니다. ISTQB CT-AI 덤프는 pdf버전과 소프트웨어버전으로만 되어있었는데 최근에는 휴대폰에서가 사용가능한 온라인버전까지 개발하였습니다. 날따라 새로운 시스템을 많이 개발하여 고객님께 더욱 편하게 다가갈수 있는 Itexamdump가 되겠습니다.

CT-AI시험대비 최신버전 문제: https://www.itexamdump.com/CT-AI.html

2026 Itexamdump 최신 CT-AI PDF 버전 시험 문제집과 CT-AI 시험 문제 및 답변 무료 공유: https://drive.google.com/open?id=1feaK0sv1A-SSQKnAheZleniyML8c0a1X