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ISTQB CT-AI Exam Overview:

Certification Vendor:	ISTQB
Exam Name:	ISTQB Certified Tester - AI Testing Exam
Exam Number:	CT-AI
Related Certifications:	ISTQB Certified Tester Foundation Level (CTFL) ISTQB Certified Tester Testing with Generative AI (CT-GenAI)
Exam Format:	1-2 points per question, Multiple-choice questions
Exam Duration:	60 (75 for non-native language)
Exam Price:	€180 - €250 (varies by region and provider)
Available Languages:	German, Portuguese, Spanish, Korean, English, French, Chinese, Japanese
Certificate Validity Period:	Valid indefinitely (no expiration)
Passing Score:	65% (29/44 points for v2.0; 31/47 points for v1.0)
Real Exam Qty:	40
Recommended Training:	ISTQB Accredited Training Providers CT-AI Syllabus v2.0
Exam Registration:	ISTQB Official Registration iSQI Exam Registration Pearson VUE
Sample Questions:	ISTQB CT-AI Sample Questions
Exam Way:	Online remote proctored / Onsite test center
Pre Condition:	Must hold ISTQB Certified Tester Foundation Level (CTFL) certification
Official Syllabus URL:	https://istqb.org/certifications/certified-tester-ai-testing-ct-ai/

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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	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 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.
Topic 4	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 5	systems from those required for conventional systems.
Topic 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.
Topic 7	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.

ISTQB Certified Tester AI Testing Exam Sample Questions (Q103-Q108):

NEW QUESTION # 103
The activation value output for a neuron in a neural network is obtained by applying computation to the neuron.
Which ONE of the following options BEST describes the inputs used to compute the activation value?
SELECT ONE OPTION

A. Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.
B. Individual bias at the neuron level, and weights assigned to the connections between the neurons.
C. Individual bias at the neuron level, and activation values of neurons in the previous layer.
D. Individual bias at the neuron level, activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.

Answer: D

Explanation:
In a neural network, the activation value of a neuron is determined by a combination of inputs from the previous layer, the weights of the connections, and the bias at the neuron level. Here's a detailed breakdown:
* Inputs for Activation Value:
* Activation Values of Neurons in the Previous Layer:These are the outputs from neurons in the preceding layer that serve as inputs to the current neuron.
* Weights Assigned to the Connections:Each connection between neurons has an associated weight, which determines the strength and direction of the input signal.
* Individual Bias at the Neuron Level:Each neuron has a bias value that adjusts the input sum, allowing the activation function to be shifted.
* Calculation:
* The activation value is computed by summing the weighted inputs from the previous layer and adding the bias.
* Formula: z=#(wi#ai)+bz = \sum (w_i \cdot a_i) + bz=#(wi#ai)+b, where wiw_iwi are the weights, aia_iai are the activation values from the previous layer, and bbb is the bias.
* The activation function (e.g., sigmoid, ReLU) is then applied to this sum to get the final activation value.
* Why Option A is Correct:
* Option A correctly identifies all components involved in computing the activation value: the individual bias, the activation values of the previous layer, and the weights of the connections.
* Eliminating Other Options:
* B. Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons: This option misses the bias, which is crucial.
* C. Individual bias at the neuron level, and weights assigned to the connections between the neurons: This option misses the activation values from the previous layer.
* D. Individual bias at the neuron level, and activation values of neurons in the previous layer: This option misses the weights, which are essential.
References:
* ISTQB CT-AI Syllabus, Section 6.1, Neural Networks, discusses the components and functioning of neurons in a neural network.
* "Neural Network Activation Functions" (ISTQB CT-AI Syllabus, Section 6.1.1).

NEW QUESTION # 104
Which statement about automation bias is correct?
Choose ONE option (1 out of 4)

A. When testing AI-based systems, automation bias does not play a role in supporting test activities such as boundary value analysis
B. Automation bias is tested with representative users, but human input quality is irrelevant
C. Automation bias particularly affects testing of autonomous systems
D. Automation bias affects the testing of AI-based systems that support users in their actions or decisions

Answer: D

Explanation:
Automation bias is defined in Section4.4 - Human Factors in AI Testingof the ISTQB CT-AI syllabus. It refers to the human tendency to overly trust, rely on, or defer to automated system outputs. The syllabus explains that this bias arises especially indecision-support systems, where humans may accept AI judgments without adequate verification. This aligns directly with Option B.
Option A is incorrect: automation biasdoesinfluence testing, especially when testers rely excessively on AI outputs. The syllabus cautions about testers adopting the same cognitive biases as end users. Option C is incorrect because autonomous systems are not the primary context; rather,systems supporting human decisionsare most impacted. Option D is incorrect because the quality of human inputmatters significantly, and poorly designed user studies can mask or distort automation bias.
Thus,Option Bis the syllabus-accurate description of automation bias.

NEW QUESTION # 105
A mobile app start-up company is implementing an AI-based chat assistant for e-commerce customers. In the process of planning the testing, the team realizes that the specifications are insufficient.
Which testing approach should be used to test this system?

A. Static analysis
B. Exploratory testing
C. State transition testing
D. Equivalence partitioning

Answer: B

Explanation:
The syllabus describes exploratory testing as especially useful when there are poor specifications or test oracle problems:
"Exploratory testing is especially useful when there are poor specifications or test oracle problems, which is often the case for AI-based systems." (Reference: ISTQB CT-AI Syllabus v1.0, Section 9.6, page 70 of 99)

NEW QUESTION # 106
You are testing an autonomous vehicle which uses AI to determine proper driving actions and responses. You have evaluated the parameters and combinations to be tested and have determined that there are too many to test in the time allowed. It has been suggested that you use pairwise testing to limit the parameters. Given the complexity of the software under test, what is likely the outcome from using pairwise testing?

A. Pairwise cannot be applied to this problem because there is AI involved and the evolving values may result in unexpected results that cannot be verified.
B. While the number of tests needed can be reduced, there may still be a large enough set of tests that automation will be required to execute all of them.
C. The number of parameters to test can be reduced to less than a dozen.
D. All high priority defects will be identified using this method.

Answer: B

Explanation:
The syllabus states that while pairwise testing is effective at finding defects by reducing the number of test cases needed, the resulting test suite can still be extensive and require automation:
"Even the use of pairwise testing can result in extensive test suites... automation and virtual test environments often become necessary to allow the required tests to be run."

NEW QUESTION # 107
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. Determining if an animal is a pig or a cow based on image recognition
D. Recognizing a knife in carry-on luggage at a security checkpoint in an airport scanner

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." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.1, Page 26 of 99)

NEW QUESTION # 108
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