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

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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 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.
Topic 3	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 4	systems from those required for conventional systems.
Topic 5	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 6	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.
Topic 7	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 8	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 9	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 10	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 11	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.

ISTQB Certified Tester AI Testing Exam Sample Questions (Q89-Q94):

NEW QUESTION # 89
You have access to the training data that was used to train an AI-based system. You can review this information and use it as a guideline when creating your tests. What type of characteristic is this?

A. Accessibility
B. Transparency
C. Autonomy
D. Explorability

Answer: B

Explanation:
AI-based systems can sometimes behave like black boxes, where the internal decision-making process is unclear. Transparency refers to the ability to inspect and understand the training data, algorithms, and decision-making process of the AI system.
Transparency ensures that testers and stakeholders can review how an AI system was trained.
Access to training data is a key factor in transparency because it allows testers to analyze biases, completeness, and representativeness of the dataset.
Transparency is an essential characteristic of explainable AI (XAI). Having access to training data means that testers can investigate how data influences AI behavior.
Regulatory and ethical AI guidelines emphasize transparency. Many AI ethics frameworks, such as GDPR and Trustworthy AI guidelines, recommend transparency to ensure fair and explainable AI decision-making.

NEW QUESTION # 90
Which of the following approaches would help overcome testing challenges associated with probabilistic and non-deterministic AI-based systems?

A. Run the test several times to generate a statistically valid test result to ensure that an appropriate number of answers are accurate
B. Decompose the system test into multiple data ingestion tests to determine if the AI system is getting precise and accurate input data
C. Run the test several times to ensure that the AI always returns the same correct test result
D. Decompose the system test into multiple data ingestion tests to determine if the AI system is getting a sufficient volume of input data

Answer: A

Explanation:
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)

NEW QUESTION # 91
You have been developing test automation for an e-commerce system. One of the problems you are seeing is that object recognition in the GUI is having frequent failures. You have determined this is because the developers are changing the identifiers when they make code updates. How could AI help make the automation more reliable?

A. It could dynamically name the objects, altering the source code, so the object names will match the object names used in the automation.
B. It could generate a model that will anticipate developer changes and pre-alter the test automation code accordingly.
C. It could modify the automation code to ignore unrecognizable objects to avoid failures.
D. It could identify the objects multiple ways and then determine the most commonly used and stable identification for each object.

Answer: D

Explanation:
The syllabus discusses using AI-based tools to reduce GUI test brittleness:
"AI can be used to reduce the brittleness of this approach, by employing AI-based tools to identify the correct objects using various criteria (e.g., XPath, label, id, class, X/Y coordinates), and to choose the historically most stable identification criteria."

NEW QUESTION # 92
Which ONE of the following activities is MOST relevant when addressing the scenario where you have more than the required amount of data available for the training?

A. Data sampling
B. Data augmentation
C. Feature selection
D. Data labeling

Answer: A

Explanation:
Data sampling involves selecting a representative subset of the data for training. When there is more data than needed, sampling can be used to create a manageable dataset that maintains the statistical properties of the full dataset.

NEW QUESTION # 93
Which of the following options BEST describes an example of reinforcement learning?

A. A financial system that chooses which stocks to buy regularly optimizes based on new changes in stock price to improve its performance.
B. A health warning system uses data from thousands of people to predict who is most likely to suffer from a particular health condition based on their behavior.
C. An image recognition system regularly searches the internet to find new examples to add to its training data.
D. A house cleaning robot adjusts the path it takes on a daily basis based on the most efficient route and the amount of dirt it is able to remove.

Answer: D

Explanation:
Reinforcement learning involves an agent learning to make decisions by taking actions that maximize a reward over time. In the example of the house cleaning robot, the robot adjusts its actions (path taken) based on feedback (efficiency and amount of dirt removed), which is characteristic of reinforcement learning.

NEW QUESTION # 94
......

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