新版CT-AI題庫 & CT-AI更新

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ISTQB CT-AI Exam Overview:

Certification Vendor:	ISTQB
Exam Name:	ISTQB Certified Tester AI Testing (CT-AI) Exam
Exam Number:	CT-AI
Exam Format:	Multiple Choice
Related Certifications:	ISTQB Certified Tester Foundation Level (CTFL)
Certificate Validity Period:	Lifetime
Passing Score:	65%
Real Exam Qty:	40
Exam Duration:	60 minutes
Available Languages:	English
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

>> 新版CT-AI題庫 <<

已驗證的新版CT-AI題庫和資格考試領導者和可靠的CT-AI：Certified Tester AI Testing Exam

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ISTQB CT-AI 考試大綱：

主題	簡介
主題 1	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.
主題 2	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.
主題 3	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.
主題 4	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.
主題 5	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
主題 6	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.
主題 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.

最新的 ISTQB AI Testing CT-AI 免費考試真題 (Q98-Q103):

問題 #98
A neural network has been designed and created to assist day-traders improve efficiency when buying and selling commodities in a rapidly changing market. Suppose the test team executes a test on the neural network where each neuron is examined. For this network, the shortest path indicates a "buy" and it will only occur when the one-day predicted value of the commodity is greater than the spot price by 0.75%. The neurons are stimulated by entering commodity prices and testers verify that they activate only when the future value exceeds the spot price by at least 0.75%.
Which of the following statements BEST explains the type of coverage being tested on the neural network?

A. Neuron coverage
B. Threshold coverage
C. Value-change coverage
D. Sign-change coverage

答案：B

解題說明：
The syllabus details that threshold coverage requires each neuron to achieve an activation value greater than a specified threshold:
"Threshold coverage: Full threshold coverage requires that each neuron in the neural network achieves an activation value greater than a specified threshold." (Reference: ISTQB CT-AI Syllabus v1.0, Section 6.2, page 48 of 99)

問題 #99
Which two test procedures are BEST suited for CleverPropose system testing?
Choose TWO options (2 out of 5)

A. Metamorphic testing
B. Adversarial testing
C. Back-to-back testing
D. Pairwise testing
E. Exploratory data analysis

答案：A,C

解題說明：
The ISTQB CT-AI syllabus explains that AI-based decision-support systems benefit strongly fromback-to- back testingandmetamorphic testingwhen oracle problems exist or when limited regression tests are available. In this scenario, CleverPropose replaces an older advisory system.Back-to-back testing(Option A) is ideal because the outputs of the existing conventional system can serve as areference, enabling comparison against the new AI system. This is exactly what the syllabus recommends when AI is replacing a traditional deterministic system.
Metamorphic testing(Option C) is also appropriate, as stated in Section4.6 - Metamorphic Relations. With limited regression tests and complex decision logic, testers can define metamorphic relations such as "if customer income increases, risk rating should not worsen." These relations allow validation even when exact expected outputs are unavailable.
Exploratory data analysis (Option D) is not a system testing technique. Pairwise testing (Option E) is not well suited for complex AI-based financial advice systems. Adversarial testing (Option B) is more relevant for security-critical or robustness evaluation, not primary system testing for advisory tools.
Thus,A and Care the correct and syllabus-supported choices.

問題 #100
Which ONE of the following types of coverage SHOULD be used if test cases need to cause each neuron to achieve both positive and negative activation values?

A. Threshold coverage
B. Value coverage
C. Neuron coverage
D. Sign change coverage

答案：D

解題說明：
Coverage for Neuron Activation Values: Sign change coverage is used to ensure that test cases cause each neuron to achieve both positive and negative activation values. This type of coverage ensures that the neurons are thoroughly tested under different activation states.

問題 #101
Which ONE of the following statements BEST describes a testing challenge that specifically applied to a self- learning system?

A. In can be difficult to explain the link between test inputs and outputs
B. It is necessary to test whether the system will relinquish control to a human at the right time
C. When systems change themselves the results of previously passing tests may change
D. External data sources might be required to ensure that the system is unbiased

答案：C

解題說明：
The correct answer is A . A self-learning system can modify its own behaviour as it learns from operational data, environmental inputs, or its own decisions. The CT-AI syllabus identifies "unexpected change" as a major testing challenge for self-learning systems. It explains that when systems change themselves and their outputs, the results of previously passing tests can change, creating a regression testing and test design challenge. Testers may need tests that remain valid as the system evolves, and may also need new tests based on newly observed behaviours.
Option B relates more directly to autonomy , where the system must know when to return control to a human operator. Option C relates to testing for inappropriate bias, particularly when hidden or inferred attributes may influence outcomes. Option D relates to explainability, where the link between an input and a model output may be difficult to understand. These are important AI testing issues, but they are not the most specific self- learning challenge.
References/topics: CT-AI Syllabus Chapter 8, Section 8.1 "Challenges Testing Self-Learning Systems."
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問題 #102
A beer company is trying to understand how much recognition its logo has in the market. It plans to do that by monitoring images on various social media platforms using a pre-trained neural network for logo detection. This particular model has been trained by looking for words, as well as matching colors on social media images. The company logo has a big word across the middle with a bold blue and magenta border. Which associated risk is most likely to occur when using this pre-trained model?

A. Improper data preparation
B. Inherited bias: the model could have inherited unknown defects
C. There is no risk, as the model has already been trained
D. Insufficient function; the model was not trained to check for colors or words

答案：B

解題說明：
According to the syllabus, pre-trained models often inherit biases and limitations from the data and processes used in their original training, which may not align with the new use case.
Specifically, the syllabus states:
"When using a pre-trained model, the training data and process cannot be fully controlled or known by the user of the model. As a result, the model can inherit biases or inaccuracies that were part of its original development and training process."

問題 #103
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