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ISTQB CT-AI Exam Overview:

Certification Vendor:	ISTQB
Exam Name:	ISTQB Certified Tester - AI Testing
Exam Number:	CT-AI
Passing Score:	65%
Certificate Validity Period:	Lifetime (no expiration)
Exam Format:	Multiple Choice
Exam Duration:	60 minutes
Related Certifications:	ISTQB CTAL-TTA ISTQB CTFL
Real Exam Qty:	40
Available Languages:	English
Exam Price:	EUR 250
Sample Questions:	ISTQB CT-AI Sample Questions
Exam Way:	Online proctored or in-person at authorized testing centers
Pre Condition:	ISTQB CTFL (Certified Tester Foundation Level) certification is recommended but not mandatory
Official Syllabus URL:	https://www.istqb.org/certifications/artificial-intelligence-testing-certification

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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 4	systems from those required for conventional systems.
Topic 5	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
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	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.
Topic 9	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 10	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.

ISTQB Certified Tester AI Testing Exam Sample Questions (Q53-Q58):

NEW QUESTION # 53
Which ONE of the following options represents a technology MOST TYPICALLY used to implement Al?
SELECT ONE OPTION

A. Search engines
B. Case control structures
C. Procedural programming
D. Genetic algorithms

Answer: D

Explanation:
* Technology Most Typically Used to Implement AI: Genetic algorithms are a well-known technique used in AI . They are inspired by the process of natural selection and are used to find approximate solutions to optimization and search problems. Unlike search engines, procedural programming, or case control structures, genetic algorithms are specifically designed for evolving solutions and are commonly employed in AI implementations.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 1.4 AI Technologies, which identifies different technologies used to implement AI.

NEW QUESTION # 54
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. Sign-change coverage
D. Value-change coverage

Answer: B

Explanation:
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)

NEW QUESTION # 55
Which of the following is correct regarding the layers of a deep neural network?

A. There is at least one internal hidden layer
B. There is only an input and output layer
C. There must be a minimum of five total layers to be considered deep
D. The output layer is not connected with the other layers to maintain integrity

Answer: A

Explanation:
A deep neural network (DNN) is a type of artificial neural network that consists of multiple layers between the input and output layers. The ISTQB Certified Tester AI Testing (CT-AI) Syllabus outlines the following characteristics of a DNN:
Structure of a Deep Neural Network:
A DNN comprises at least three types of layers:
Input layer: Receives the input data.
Hidden layers: Perform complex feature extraction and transformations.
Output layer: Produces the final prediction or classification.

NEW QUESTION # 56
Which ONE of the following statements about the hardware used to implement ML systems is MOST likely to be correct?

A. Support for complex operations is required
B. A higher clock speed is required
C. Specialist hardware is necessary for ML
D. Less bits are required for hardware supporting ML

Answer: D

Explanation:
The correct answer is B. Less bits are required for hardware supporting ML . The CT-AI syllabus explains that ML typically benefits from hardware supporting low-precision arithmetic , which uses fewer bits for computation, for example 8 bits instead of 32 bits, and states that this is usually sufficient for ML. It also identifies two other beneficial hardware attributes: the ability to work with large data structures and massively parallel processing.
Option A is too absolute. Specialist hardware may be useful, and AI-specific processors exist, but the syllabus states that a model may run on a low-end smartphone and that cloud training with later deployment to a host device is common. Option C is incorrect because general-purpose CPUs support complex operations that are not typically required for ML applications. Option D is incorrect because higher clock speed alone is not the deciding factor; the syllabus notes that GPUs may outperform CPUs for ML despite CPUs often having faster clock speeds.
References/topics: CT-AI Syllabus Chapter 1, Section 1.6 "Hardware for AI-Based Systems."
=========

NEW QUESTION # 57
Which of the following statements regarding experience-based testing for AI-based systems is correct?
Choose ONE option (1 out of 4)

A. Exploratory testing is often used for AI-based systems because there are often insufficient specifications or problems with the test oracle for AI-based systems.
B. Tour refers to intuitive test case design for AI-based systems based on multiple, sequential test cases using systematically biased training data.
C. Intuitive test case design for AI-based systems involves interactive, hypothesis-driven examination of data for correlations or developmental trends.
D. In checklist-based testing of AI-based systems, the existing test cases are dynamically adapted, for example based on metamorphic testing.

Answer: A

Explanation:
The ISTQB CT-AI syllabus explains inSection 4.4 - Experience-Based Testing for AI Systemsthat AI- based systems frequently suffer frominsufficient specifications, unpredictable model behavior, andtest oracle problems, especially when outputs depend on probabilistic or learned patterns. The syllabus explicitly states thatexploratory testingis especially valuable in such contexts because it allows testers to investigate the system interactively, observe unexpected behavior, and evaluate system responses that cannot be fully predicted beforehand. Thus, OptionCaccurately reflects the role and justification of exploratory testing for AI systems.
Option A describes data analysis rather than intuitive test design. Option B is incorrect because checklist- based testing does not dynamically adapt test cases; instead, it follows predetermined checklists. Option D incorrectly defines "tour-based testing"; tours refer to structured exploratory approaches, not biased datasets.
Therefore,Option Cis the syllabus-aligned correct statement.

NEW QUESTION # 58
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