ISTQB CT-AI Questions Pdf | CT-AI Reliable Practice Materials

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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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.
Topic 2	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.
Topic 3	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.
Topic 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.
Topic 5	systems from those required for conventional systems.
Topic 6	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.
Topic 7	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 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.
Topic 9	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based

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Authentic ISTQB CT-AI Exam Questions with Answers

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q114-Q119):

NEW QUESTION # 114
Which of the following decisions is BEST as a test approach for the described situation?
Choose ONE option (1 out of 4)

A. You plan to manually execute regular regression tests of the new camera function, particularly for system tests.
B. You plan to perform reviews and exploratory data analysis of the image data sets to reduce the risk of a lack of representativeness of this data.
C. You plan experience-based testing by the entire team at the system test level to ensure that the end users are satisfied.
D. You execute the test cases from the old camera model at the integration test level; no further dynamic tests of the operating data pipeline are necessary.

Answer: C

Explanation:
The ISTQB CT-AI syllabus emphasizes that testing AI-based systems requirescross-functional collaboration andexperience-based testingwhen parts of the team lack domain knowledge. In this scenario, the ML expert understands ML and dataset preparation but lacks knowledge ofcamera system behavior, the device's operational data pipeline, and end-user workflows. The remainder of the team understands the domain and system testing but not ML. Section4.4 - Human Factors and AI Testingand4.3 - System Testing of AI Componentshighlight that when domain understanding is unevenly distributed,experience-based testing conducted by the full team(testers, developers, domain experts) is the most effective approach. This ensures that AI outputs align with actual user expectations and system behavior. OptionCaligns exactly with this principle.
Option A is too limited and does not address the need to validate ML integration. Option B is incorrect because reusing old test cases overlooks AI-specific risks in the operating data pipeline. Option D is useful but focuses only on data representativeness, not system-level user validation. Therefore,Option Cis the best, syllabus-aligned test approach.

NEW QUESTION # 115
Written requirements are given in text documents, which ONE of the following options is the BEST way to generate test cases from these requirements?
SELECT ONE OPTION

A. Machine learning on logs of execution
B. Natural language processing on textual requirements
C. GUI analysis by computer vision
D. Analyzing source code for generating test cases

Answer: B

Explanation:
When written requirements are given in text documents, the best way to generate test cases is by using Natural Language Processing (NLP). Here's why:
* Natural Language Processing (NLP): NLP can analyze and understand human language. It can be used to process textual requirements to extract relevant information and generate test cases. This method is efficient in handling large volumes of textual data and identifying key elements necessary for testing.
* Why Not Other Options:
* Analyzing source code for generating test cases: This is more suitable for white-box testing where the code is available, but it doesn't apply to text-based requirements.
* Machine learning on logs of execution: This approach is used for dynamic analysis based on system behavior during execution rather than static textual requirements.
* GUI analysis by computer vision: This is used for testing graphical user interfaces and is not applicable to text-based requirements.
References:This aligns with the methodology discussed in the syllabus under the section on using AI for generating test cases from textual requirements.

NEW QUESTION # 116
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?

A. Value change coverage
B. Sign-change coverage
C. None of the coverage measures
D. Neuron coverage

Answer: A

Explanation:
The correct answer is B. Value change coverage . The CT-AI syllabus defines value-change coverage as requiring test cases to cause each neuron to achieve two activation values , where the difference between the two values exceeds a chosen value.
The question states that the test engineer wants to count neurons that have produced two activation function results with a minimum difference of 0.5 . This is precisely the value-change coverage concept: the selected threshold is 0.5, and a neuron is covered when its activation values vary by at least that threshold. Option A, neuron coverage, only requires neurons to achieve an activation value greater than zero; it does not assess the magnitude of change between two outputs. Option C, sign-change coverage, requires neurons to achieve both positive and negative activation values, which focuses on sign rather than numerical distance. Option D is incorrect because the syllabus explicitly includes a coverage criterion matching the described objective.
References/topics: CT-AI Syllabus Chapter 6, Section 6.2 "Coverage Measures for Neural Networks."
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NEW QUESTION # 117
Which of the following problems would best be solved using the supervised learning category of regression?

A. Determining the optimal age for a chicken's egg laying production using input data of the chicken's age and average daily egg production for one million chickens.
B. Predicting shopper purchasing behavior based on the category of shopper and the positioning of promotional displays within a store.
C. Recognizing a knife in carry on luggage at a security checkpoint in an airport scanner.
D. Determining if an animal is a pig or a cow based on image recognition.

Answer: A

Explanation:
The syllabus states:
"Supervised learning... divides problems into two categories: classification and regression.
Regression is used when the problem requires the ML model to predict a numeric output, for example predicting the age of a person based on their habits."

NEW QUESTION # 118
A local business has a mail pickup/delivery robot for their office. The robot currently uses a track to move between pickup/drop off locations. When it arrives at a destination, the robot stops to allow a human to remove or deposit mail.
The office has decided to upgrade the robot to include AI capabilities that allow the robot to perform its duties without a track, without running into obstacles, and without human intervention.
The test team is creating a list of new and previously established test objectives and acceptance criteria to be used in the testing of the robot upgrade. Which of the following test objectives will test an AI quality characteristic for this system?

A. The robot must recharge for no more than six hours a day
B. The robot must complete 99.99% of its deliveries each day
C. The robot must record the time of each delivery which is compiled into a report
D. The robot must evolve to optimize its routing

Answer: D

Explanation:
AI-based systems have specific quality characteristics, includingevolution,autonomy, andadaptability. A test objective that evaluates whether an AI systemevolvesto improve performance over time directly aligns with AI quality characteristics.
Explanation of Answer Choices:
* Option A: The robot must evolve to optimize its routing.
* Correct.Evolution is an AI quality characteristic that ensures the systemlearns from past experiencesand adapts to improve efficiency.
* Option B: The robot must recharge for no more than six hours a day.
* Incorrect.This is an operational constraint rather than an AI-specific quality characteristic.
* Option C: The robot must record the time of each delivery which is compiled into a report.
* Incorrect.Logging data does not relate to AI quality characteristics likeadaptability or autonomy.
* Option D: The robot must complete 99.99% of its deliveries each day.
* Incorrect.This is a performance target rather than an AI quality characteristic.
ISTQB CT-AI Syllabus References:
* Evolution as an AI Quality Characteristic:"Check how well the system learns from its own experience. Check how well the system copes when the profile of data changes (i.e., concept drift)".
Thus,Option A is the best choice as it directly tests an AI quality characteristic (evolution) in the upgraded autonomous robot.

NEW QUESTION # 119
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