최신업데이트된CT-AI최신기출자료시험대비자료

ExamPassdump CT-AI 최신 PDF 버전 시험 문제집을 무료로 Google Drive에서 다운로드하세요: https://drive.google.com/open?id=1Itut-9DzykNFVVavenLtK3Er8KS7U14b

ExamPassdump를 검색을 통해 클릭하게된 지금 이 순간 IT인증자격증취득ISTQB CT-AI시험은 더는 힘든 일이 아닙니다. 다른 분들이ISTQB CT-AI시험준비로 수없는 고민을 할때 고객님은 저희 ISTQB CT-AI덤프로 제일 빠른 시일내에 시험을 패스하여 자격증을 손에 넣을수 있습니다.

ISTQB CT-AI 시험요강:

주제소개
주제 1
  • systems from those required for conventional systems.
주제 2
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
주제 3
  • 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.
주제 4
  • 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 5
  • Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.
주제 6
  • Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.
주제 7
  • Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.
주제 8
  • 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 9
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
주제 10
  • 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 11
  • Testing AI-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.

>> CT-AI최신 기출자료 <<

CT-AI최고품질 시험대비자료, CT-AI합격보장 가능 공부자료

ExamPassdump의ISTQB CT-AI 덤프 구매 후 등록된 사용자가 구매일로부터 일년 이내에ISTQB CT-AI시험에 실패하셨다면 ExamPassdump메일에 주문번호와 불합격성적표를 보내오셔서 환불신청하실수 있습니다.구매일자 이전에 발생한 시험불합격은 환불보상의 대상이 아닙니다. 개별 인증사는 불합격성적표를 발급하지 않기에 재시험신청내역을 환불증명으로 제출하시면 됩니다.

최신 ISTQB AI Testing CT-AI 무료샘플문제 (Q73-Q78):

질문 # 73
Which of the following approaches would help overcome testing challenges associated with probabilistic and non-deterministic AI-based systems?

정답：D

설명：
The syllabus states:
"When testing probabilistic and non-deterministic systems, the same input may produce different outputs.
Tests need to be run several times to produce statistically valid test results, ensuring that an appropriate number of answers are accurate." (Reference: ISTQB CT-AI Syllabus v1.0, Section 8.4, page 58 of 99)


질문 # 74
Which of the following aspects is a challenge when handling test data for an AI-based system?

정답：A

설명：
The syllabus explicitly mentions challenges of handling personal data and ensuring privacy when testing AI- based systems:
"The management of personal data and sensitive data is often a concern during testing, as testing typically requires realistic data and it is difficult to fully anonymize data." (Reference: ISTQB CT-AI Syllabus v1.0, Section 7.3, page 52 of 99)


질문 # 75
Which AI-specific test objective and acceptance criterion should be selected MOST LIKELY for testing GPT_Legal?
Choose ONE option (1 out of 4)

정답：D

설명：
The ISTQB CT-AI syllabus introducesAI-specific quality characteristics, includingevolution,functional safety,compatibility, andbias-related data quality. Section5.1 - AI-Specific Test Objectivesexplains that evolutionrefers to an AI system's capability to continue improving or at least maintain performance as it undergoes additional training. GPT_Legal is explicitly described as aself-learning systemexpected to:
* continuously reduce false positives,
* achieve weekly accuracy improvements of 10%,
* reach and maintain 90% accuracy,
* adapt to new environments (patent law firm # corporate legal department).
This aligns perfectly with the syllabus definition ofevidence of evolution: ensuring the model doesnot degrade as additional training data is introduced. OptionBtherefore directly supports the described acceptance criteria for this evolving, self-learning application.
Option A (functional safety) is irrelevant because patent searching and drafting do not constitute safety- critical domains. Option C (compatibility) is necessary but not the primary AI-specific objective. Option D addresses bias, which is important but not central to the described performance and continuous-learning expectations.
Thus,Option Bis the most appropriate AI-specific test objective.


질문 # 76
Which ONE of the following statements BEST describes a testing challenge that specifically applied to a self-learning system?

정답：D

설명：
A key challenge in testing self-learning systems is that, as the system learns and adapts over time, the results of previously passing tests may change. This is because the system's behavior evolves as it learns from new data, potentially altering how it responds to test inputs. This dynamic nature of self-learning systems makes it challenging to maintain consistent and reliable test results.


질문 # 77
You are evaluating the use of a highly accurate pre trained model that is used widely in industry tor a similar use case. There is an intention to apply transfer learning techniques to further customise the model. Which ONE of the following is the LEAST likely to be a significant risk with this approach?

정답：D

설명：
The correct answer is D because the scenario explicitly reduces that risk: the model is described as highly accurate , widely used in industry, and applied to a similar use case. The CT-AI syllabus explains that using a pre-trained model can save training costs and largely eliminate the risk of it not working when an existing suitable model is reused. It also states that transfer learning effectiveness depends heavily on similarity between the original model's function and the new required function.
Options A, B, and C remain significant risks under the syllabus. A is credible because shortcomings such as inherited biases may pass from the pre-trained model to the reused system. B is credible because models created through transfer learning are highly likely to remain sensitive to vulnerabilities of the pre-trained model, including adversarial attacks. C is credible because differences in data preparation steps between original model development and later use may reduce functional performance. Therefore, the least likely significant risk is unexpectedly low functional performance of the already highly accurate, widely used, similar-purpose pre-trained model.
References/topics: CT-AI Syllabus Chapter 1, Sections 1.8.1-1.8.3 "Pre-Trained Models," "Transfer Learning," and "Risks of using Pre-Trained Models and Transfer Learning."
=========


질문 # 78
......

ExamPassdump에서는 전문ISTQB CT-AI인증시험을 겨냥한 덤프 즉 문제와 답을 제공합니다.여러분이 처음ISTQB CT-AI인증시험준비라면 아주 좋은 덤프입니다. ExamPassdump에서 제공되는 덤프는 모두 실제시험과 아주 유사한 덤프들입니다.ISTQB CT-AI인증시험패스는 보장합니다. 만약 떨어지셨다면 우리는 덤프비용전액을 환불해드립니다.

CT-AI최고품질 시험대비자료: https://www.exampassdump.com/CT-AI_valid-braindumps.html

참고: ExamPassdump에서 Google Drive로 공유하는 무료, 최신 CT-AI 시험 문제집이 있습니다: https://drive.google.com/open?id=1Itut-9DzykNFVVavenLtK3Er8KS7U14b