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ISTQB CT-AI Exam Syllabus Topics:

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

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q17-Q22):

NEW QUESTION # 17
Which statement regarding the use of training, validation, and test data sets is correct?
Choose ONE option (1 out of 4)

A. Optimally, the data should be distributed equally between the training, validation, and test data sets.
B. If only limited data is available, validation and test data sets can be combined in multiple ways during training.
C. If limited data is available, it may be better to work without a separate test data set.
D. The data in the test data set must be equivalent to the data in the training data sets and to the data in the validation data sets.

Answer: D

Explanation:
The ISTQB CT-AI syllabus (Section3.2 - Model Evaluation) specifies the correct usage oftraining, validation, andtestdatasets. It emphasizes that thetest dataset must be representative of the real operational dataand must beequivalent in distribution to the training and validation sets, ensuring a fair and unbiased evaluation. Option D precisely matches this requirement.
Option A contradicts the syllabus because validation and test sets servedifferent purposes: validation is for tuning, test is for final evaluation. Combining them undermines the reliability of results. Option B is incorrect because even with limited data, the syllabus recommends maintaining a test set or using techniques such as cross-validationrather than eliminating testing. Option C is wrong because equal distribution (33/33/33) isnot recommended; typically, the training set is much larger (e.g., 70-80%).
Thus, OptionDis the only statement aligned with the syllabus' guidance.

NEW QUESTION # 18
A car insurance company is using a new AI service to reward defensive driving behavior among its policyholders. The driving behavior is recorded in a rating number (score).
The AI service determines this score from the following input values:
Reference speed v_max in km/h
Average speed v_mean in km/h
Average acceleration a_pos in m/s²
Average braking deceleration a_neg in m/s²
The more defensive the driving behavior is (slow driving, low acceleration, low braking deceleration), the higher is the score.
Three initial test cases (Test 1 to Test 3) are used for testing the AI service. In addition, new test cases A-D are proposed.

Which of the new tests is NOT a follow-up test case for metamorphic testing?
Choose ONE option! (1 out of 4)

A. Test C is not a follow-up test case.
B. Test A is not a follow-up test case.
C. Test D is not a follow-up test case.
D. Test B is not a follow-up test case.

Answer: D

Explanation:
According to the ISTQB CT-AI syllabus,metamorphic testingworks by applyingmetamorphic relations (MRs): predictable input transformations that should lead to predictable output changes. From the initial test data, clear relations emerge for defensive driving scoring. The score increases when:
v_mean decreases,
a_pos decreases,
a_neg becomes less negative,and decreases when the opposite occurs.
A valid metamorphic follow-up test must modify inputs in a direction consistent with at least one MR while keeping the expected output direction predictable.
Test Alowers v_mean compared to Test 1, with similar acceleration values. This directly satisfies the MR that lower speed # higher score.
Test Cincreases both acceleration and braking intensity compared to Test 2, making the reduced score range (30-70) consistent with more aggressive driving.
Test Dmodifies acceleration and braking magnitudes in ways consistent with Test 3's defensive-driving scoring boundaries.
Test B, however, changes multiple variables in contradictory directions:
v_mean increases (worse)
a_pos increases (worse)
a_neg becomes less negative (better)
Because these changes conflict,the expected score trend becomes unpredictable, violating the premise of a metamorphic follow-up test.
ThusTest B cannot be considered a metamorphic follow-up, which makesOption Ccorrect.

NEW QUESTION # 19
Which statement describes factors related to test data that make testing AI-based systems difficult?

A. Creating and managing large amounts of test data can be difficult, especially when it needs to be representative
B. Using the same implementation for data acquisition by data scientists and testers prevents defect masking
C. Artificially generated data requires legal approval and must be sanitized and encrypted
D. The input data must always be the same over time, especially in real-world systems

Answer: A

Explanation:
Section2.2 - Data Preparationand4.1 - Challenges in Testing AI-Based Systemsdescribe difficulties in obtaining and managing large, representative datasets. AI-based systems requirerealistic, diverse, and representativedata reflecting real-world variations. The syllabus emphasizes that assembling such datasets is time-consuming, resource-intensive, and often constrained by availability, privacy, or domain complexity. Option B directly corresponds to these documented challenges.

NEW QUESTION # 20
Which of the following is correct regarding the layers of a deep neural network?

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

Answer: C

Explanation:
Adeep neural network (DNN)is a type of artificial neural network that consists of multiple layers between the input and output layers. TheISTQB Certified Tester AI Testing (CT-AI) Syllabusoutlines the following characteristics of a DNN:
* Structure of a Deep Neural Network:
* ADNN 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.
* Analysis of Answer Choices:
* A (Only input and output layers)# Incorrect, as a DNN must haveat least one hidden layer.
* B (At least one internal hidden layer)# Correct, as a neural network must havehidden layersto be considered deep.
* C (Minimum of five layers required)# Incorrect, asthere is no strict definitionthat requires at least five layers.
* D (Output layer is not connected to other layers)# Incorrect, asthe output layer must be connectedto the hidden layers.
Thus,Option B is the correct answer, asa deep neural network must have at least one hidden layer.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 6.1 (Neural Networks and Deep Neural Networks)
* ISTQB CT-AI Syllabus v1.0, Section 6.2 (Structure of Deep Neural Networks).

NEW QUESTION # 21
Which of the following are the three activities in the data acquisition activities for data preparation?

A. Cleaning, transforming, augmenting
B. Building, approving, deploying
C. Identifying, gathering, labelling
D. Feature selecting, feature growing, feature augmenting

Answer: C

Explanation:
The syllabus defines data acquisition as consisting of three steps:
"Data acquisition: The activity of acquiring data relevant to the business problem to be solved by an ML model, typically involving the activities of identifying, gathering and labelling data." (Reference: ISTQB CT-AI Syllabus v1.0, Section 4.1, page 33 of 99)

NEW QUESTION # 22
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