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2026 ExamPassdump 최신 CT-AI PDF 버전 시험 문제집과 CT-AI 시험 문제 및 답변 무료 공유: https://drive.google.com/open?id=1iBsBhOzFGHPdWgZ7y04zF2jmXA9AlHM8

ExamPassdump의ISTQB CT-AI교육 자료는 고객들에게 높게 평가 되어 왔습니다. 그리고 이미 많은 분들이 구매하셨고ISTQB CT-AI시험에서 패스하여 검증된 자료임을 확신 합니다. ISTQB CT-AI시험을 패스하여 자격증을 취득하면IT 직종에 종사하고 계신 고객님의 성공을 위한 중요한 요소들 중의 하나가 될 것이라는 것을 잘 알고 있음으로 더욱 믿음직스러운 덤프로 거듭나기 위해 최선을 다해드리겠습니다.

ISTQB CT-AI 시험요강:

주제소개
주제 1
  • 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.
주제 2
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
주제 3
  • 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 4
  • 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 5
  • systems from those required for conventional systems.
주제 6
  • 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 7
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
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.

>> CT-AI덤프 <<

CT-AI덤프 100％시험패스 자료

IT인증자격증은 국제적으로 승인받는 자격증이기에 많이 취득해두시면 취업이나 승진이나 이직이나 모두 편해집니다. 다른 사람이 없는 자격증을 내가 가지고 있다는것은 실력을 증명해주는 수단입니다. ISTQB인증 CT-AI시험은 널리 승인받는 자격증의 시험과목입니다. ISTQB인증 CT-AI덤프로ISTQB인증 CT-AI시험공부를 하시면 시험패스 난이도가 낮아지고 자격증 취득율이 높이 올라갑니다.자격증을 많이 취득하여 취업이나 승진의 문을 두드려 보시면 빈틈없이 닫힌 문도 활짝 열릴것입니다.

최신 ISTQB AI Testing CT-AI 무료샘플문제 (Q22-Q27):

질문 # 22
Which ONE of the following options is the MOST APPROPRIATE stage of the ML workflow to set model and algorithm hyperparameters?
SELECT ONE OPTION

정답：C

설명：
Setting model and algorithm hyperparameters is an essential step in the machine learning workflow, primarily occurring during the tuning phase.
* Evaluating the model (A): This stage involves assessing the model's performance using metrics and does not typically include the setting of hyperparameters.
* Deploying the model (B): Deployment is the stage where the model is put into production and used in real-world applications. Hyperparameters should already be set before this stage.
* Tuning the model (C): This is the correct stage where hyperparameters are set. Tuning involves adjusting the hyperparameters to optimize the model's performance.
* Data testing (D): Data testing involves ensuring the quality and integrity of the data used for training and testing the model. It does not include setting hyperparameters.
Hence, the most appropriate stage of the ML workflow to set model and algorithm hyperparameters isC.
Tuning the model.
References:
* ISTQB CT-AI Syllabus Section 3.2 on the ML Workflow outlines the different stages of the ML process, including the tuning phase where hyperparameters are set.
* Sample Exam Questions document, Question #31 specifically addresses the stage in the ML workflow where hyperparameters are configured.


질문 # 23
Which of the following is a dataset issue that can be resolved using pre-processing?

정답：D

설명：
The syllabus describes that data pre-processing includes cleaning (e.g., fixing or removing invalid data) and transforming data (e.g., changing data types such as numbers stored as strings).
"Transformation: The format of the given data is changed... converting categorical data into numerical data, changing image formats..."


질문 # 24
Which ONE of the following statements correctly describes the importance of flexibility for Al systems?

정답：C

설명：
Flexibility in AI systems is crucial for various reasons, particularly because it allows for easier modification and adaptation of the system as a whole.
AI systems are inherently flexible (A): This statement is not correct. While some AI systems may be designed to be flexible, they are not inherently flexible by nature. Flexibility depends on the system's design and implementation.
AI systems require changing operational environments; therefore, flexibility is required (B): While it's true that AI systems may need to operate in changing environments, this statement does not directly address the importance of flexibility for the modification of the system.
Flexible AI systems allow for easier modification of the system as a whole (C): This statement correctly describes the importance of flexibility. Being able to modify AI systems easily is critical for their maintenance, adaptation to new requirements, and improvement.
Self-learning systems are expected to deal with new situations without explicitly having to program for it (D): This statement relates to the adaptability of self-learning systems rather than their overall flexibility for modification.
Hence, the correct answer is C. Flexible AI systems allow for easier modification of the system as a whole.


질문 # 25
While measuring the test coverage of a neural network, a test engineer wants to measure the number of neurons that have each output two activation function results with a minimum difference between the two results of 0.5.
Which ONE of the below coverage measures would achieve that goal?

정답：A

설명：
Value-change coverage is a coverage measure that focuses on tracking the changes in the values of a neuron's output. In this case, the test engineer is interested in measuring the number of neurons that exhibit a difference of 0.5 between two activation function results, which is effectively a value change. This coverage measure ensures that the neuron undergoes a significant change in its output, fulfilling the engineer's goal.


질문 # 26
A wildlife conservation group would like to use a neural network to classify images of different animals. The algorithm is going to be used on a social media platform to automatically pick out pictures of the chosen animal of the month. This month's animal is set to be a wolf. The test team has already observed that the algorithm could classify a picture of a dog as being a wolf because of the similar characteristics between dogs and wolves. To handle such instances, the team is planning to train the model with additional images of wolves and dogs so that the model is able to better differentiate between the two. What test method should you use to verify that the model has improved after the additional training?

정답：B

설명：
The syllabus defines back-to-back testing as a method to compare a modified AI system to the previous version, which is ideal in this scenario:
"Back-to-back testing is performed by comparing the outputs of two systems that are supposed to provide the same outputs, one being a known and trusted system and the other being the test system. This approach can be used to test ML systems after re-training to verify that improvements have not introduced regressions."


질문 # 27
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CT-AI인증시험은ISTQB인증시험중의 하나입니다.그리고 또한 비중이 아주 큰 인증시험입니다. 그리고ISTQB CT-AI인증시험 패스는 진짜 어렵다고 합니다. 우리ExamPassdump에서는 여러분이CT-AI인증시험을 편리하게 응시하도록 전문적이 연구팀에서 만들어낸 최고의CT-AI덤프를 제공합니다, ExamPassdump와 만남으로 여러분은 아주 간편하게 어려운 시험을 패스하실 수 있습니다,

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