높은적중율을자랑하는CT-AI시험대비덤프데모문제덤프문제

2026 Itcertkr 최신 CT-AI PDF 버전 시험 문제집과 CT-AI 시험 문제 및 답변 무료 공유: https://drive.google.com/open?id=1be3gourISzV42kfMjyVJe8yLK_9rINoq

제일 빠른 시일내에 제일 간단한 방법으로ISTQB인증 CT-AI시험을 패스하는 방법이 없냐구요? Itcertkr의ISTQB인증 CT-AI덤프를 공부하시면 가능합니다. Itcertkr의ISTQB인증 CT-AI덤프는 많은 분들이 검증한 가장 유력한ISTQB인증 CT-AI시험공부자료입니다. 덤프의 문제만 기억하시면 패스는 문제없기에 제일 빠른 시일내에 시험을 패스하여 자격증 취득이 가능합니다.

ISTQB CT-AI Exam Overview:

Certification Vendor:	ISTQB
Exam Name:	ISTQB Certified Tester AI Testing (CT-AI) Exam
Exam Number:	CT-AI
Related Certifications:	ISTQB Certified Tester Foundation Level (CTFL)
Real Exam Qty:	40
Certificate Validity Period:	Lifetime
Exam Duration:	60 minutes
Available Languages:	English
Passing Score:	65%
Exam Format:	Multiple Choice
Recommended Training:	ISTQB Accredited Training Providers
Exam Registration:	ISTQB Official Website
Sample Questions:	ISTQB CT-AI Sample Questions
Exam Way:	Online and onsite proctored exam via accredited ISTQB examination providers
Pre Condition:	Recommended prior knowledge of ISTQB Foundation Level (CTFL) and basic understanding of software testing concepts and machine learning fundamentals.
Official Syllabus URL:	https://www.istqb.org

>> CT-AI시험대비 덤프데모문제 <<

CT-AI시험대비 덤프데모문제 최신버전 덤프

그렇게 많은 IT인증덤프공부자료를 제공하는 사이트중Itcertkr의 인지도가 제일 높은 원인은 무엇일가요?그건Itcertkr의 제품이 가장 좋다는 것을 의미합니다. Itcertkr에서 제공해드리는 ISTQB인증 CT-AI덤프공부자료는ISTQB인증 CT-AI실제시험문제에 초점을 맞추어 시험커버율이 거의 100%입니다. 이 덤프만 공부하시면ISTQB인증 CT-AI시험패스에 자신을 느끼게 됩니다.

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	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 3	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
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	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 6	ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.
주제 7	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.

최신 ISTQB AI Testing CT-AI 무료샘플문제 (Q35-Q40):

질문 # 35
Which ONE of the following statements is true about dynamic testing for inappropriate bias?

A. Inappropriate bias only needs to be tested when protected characteristics such as race and gender are present in the inputs
B. Reviewing the source of the training data can reveal inappropriate bias
C. It can be necessary to obtain additional attributes about the data being processed
D. Testing should never be conducted in production

정답：C

질문 # 36
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?

A. Back-to-back testing using the version of the model before training and the new version of the model after being trained with additional images.
B. Adversarial testing to verify that no incorrect images have been used in the training.
C. Metamorphic testing because the application domain is not clearly understood at this point.
D. Pairwise testing using combinatorics to look at a long list of photo parameters.

정답：A

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 # 37
"Splendid Healthcare" has started developing a cancer detection system based on ML. The type of cancer they plan on detecting has 2% prevalence rate in the population of a particular geography. It is required that the model performs well for both normal and cancer patients.
Which ONE of the following combinations requires MAXIMIZATION?
SELECT ONE OPTION

A. Maximize accuracy and recall
B. Maximize specificity number of classes
C. Maximize recall and precision
D. Maximize precision and accuracy

정답：C

설명：
* Prevalence Rate and Model Performance:
* The cancer detection system being developed by "Splendid Healthcare" needs to account for the fact that the type of cancer has a 2% prevalence rate in the population. This indicates that the dataset is highly imbalanced with far fewer positive (cancer) cases compared to negative (normal) cases.
* Importance of Recall:
* Recall, also known as sensitivity or true positive rate, measures the proportion of actual positive cases that are correctly identified by the model. In medical diagnosis, especially cancer detection, recall is critical because missing a positive case (false negative) could have severe consequences for the patient. Therefore, maximizing recall ensures that most, if not all, cancer cases are detected.
* Importance of Precision:
* Precision measures the proportion of predicted positive cases that are actually positive. High precision reduces the number of false positives, meaning fewer people will be incorrectly diagnosed with cancer. This is also important to avoid unnecessary anxiety and further invasive testing for those who do not have the disease.
* Balancing Recall and Precision:
* In scenarios where both false negatives and false positives have significant consequences, it is crucial to balance recall and precision. This balance ensures that the model is not only good at detecting positive cases but also accurate in its predictions, reducing both types of errors.
* Accuracy and Specificity:
* While accuracy (the proportion of total correct predictions) is important, it can be misleading in imbalanced datasets. In this case, high accuracy could simply result from the model predicting the majority class (normal) correctly. Specificity (true negative rate) is also important, but for a cancer detection system, recall and precision take precedence to ensure positive cases are correctly and accurately identified.
* Conclusion:
* Therefore, for a cancer detection system with a low prevalence rate, maximizing both recall and precision is crucial to ensure effective and accurate detection of cancer cases.
This explanation aligns with the principles outlined in the ISTQB CT-AI Syllabus, particularly sections on performance metrics for ML models and handling imbalanced datasets (Chapter 5: ML Functional Performance Metrics).

질문 # 38
Which statement about the property of the test environment for an AI-based system is correct?

A. The test environment for an AI system may need to include tools that can explain the decisions of the test object.
B. The test environment for a self-learning AI system needs to adapt to and learn from the test object.
C. The test environment for an autonomous AI system needs to perform both test design and execution autonomously.
D. The test environment for an AI-based multi-agent system needs to act deterministically.

정답：A

설명：
The ISTQB CT-AI syllabus (Section4.3 - Test Environments for AI Systems) describes that, unlike conventional software testing, testing AI systems may require specialized toolsfor analyzing and explaining the decisions of ML models. This includes visualization tools, explainability frameworks, and diagnostic utilities to understand why the AI made a certain prediction. Since AI decisions may be non-transparent, the test environment must supportexplainability, making Option B correct.

질문 # 39
Which assignment of AI techniques to testing support is BEST?

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

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 # 40
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