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ISTQB CT-AI Exam Syllabus Topics:

Section	Objectives
Topic 1: Ethics and Risk in AI Testing	- Ethical considerations 1. Responsible AI principles 2. Privacy and security concerns - Risk-based testing for AI 1. Mitigation strategies 2. Risk identification in AI systems
Topic 2: AI System Lifecycle and Operations	- Deployment and monitoring 1. Model deployment strategies 2. Post-deployment monitoring - Continuous learning systems 1. Model drift detection 2. Retraining strategies
Topic 3: Data Quality and Bias	- Data quality assurance 1. Data completeness and consistency 2. Data labeling quality - Bias and fairness 1. Fairness testing approaches 2. Types of bias in AI systems
Topic 4: AI Quality Characteristics	- Quality attributes 1. Explainability and transparency 2. Accuracy and robustness
Topic 5: Testing AI-Based Systems	- Test design techniques 1. Data-driven test design 2. Metamorphic testing - Test levels for AI systems 1. System integration testing 2. Model testing
Topic 6: Introduction to AI Testing	- AI systems overview 1. Differences between traditional and AI-based systems 2. What is AI and machine learning systems - Challenges in AI testing 1. Non-determinism in AI systems 2. Data dependency issues
Topic 7: Machine Learning Fundamentals for Testing	- Model types 1. Deep learning basics 2. Supervised and unsupervised learning - ML lifecycle 1. Training, validation, and evaluation 2. Data collection and preparation

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q13-Q18):

NEW QUESTION # 13
Which of the following technologies for implementing AI is considered to be a reasoning technique?
Choose ONE option (1 out of 4)

A. Linear regression
B. Random Forest
C. Deductive classifiers
D. Genetic algorithms

Answer: C

Explanation:
TheISTQB Certified Tester AI Testing Syllabus v1.0explicitly categorizes different AI implementation technologies in Section1.4 - AI Technologies. Within this section, AI methods are grouped into categories, one of which is"Reasoning techniques."These reasoning techniques includerule engines, deductive classifiers, case-based reasoning, and procedural reasoning. Because deductive classifiers are directly listed under this set of reasoning approaches, they are recognized as a reasoning-based AI technology.
Reasoning techniques differ from machine learning approaches because they rely onstructured, predefined rules or logicto reach conclusions. Deductive classifiers use logical inference and symbolic reasoning to classify inputs by applying encoded knowledge. This makes them fundamentally different from statistical or data-driven ML algorithms.
The other options-Linear regression,Random Forest, andGenetic algorithms-are listed by the syllabus asmachine learning techniques, not reasoning methods. Linear regression performs numerical prediction, Random Forest is an ensemble decision-tree ML model, and genetic algorithms are optimization-based ML approaches inspired by evolutionary processes. None of these involve symbolic logical deduction.
Thus, based on the authoritative definitions in the syllabus,Deductive classifiers (Option A)is the only technology classified as a reasoning technique.

NEW QUESTION # 14
Which ONE of the following would be the LEAST effective input to an AI-based test optimization process?

A. Test environment downtime
B. Source control data
C. Defect reports
D. Previously failing tests

Answer: A

Explanation:
Test environment downtime would be the least effective input to an AI-based test optimization process. While downtime may affect the ability to run tests, it does not provide direct insight into the quality of the tests or the effectiveness of the AI model being tested. In contrast, previously failing tests, defect reports, and source control data are much more relevant as they provide information about areas where the system may have issues or require further optimization.

NEW QUESTION # 15
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. The output layer is not connected with the other layers to maintain integrity
C. There is only an input and output layer
D. There is at least one internal hidden layer

Answer: D

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 # 16
Which statement regarding pairwise testing in an AI-based automotive lane-keeping assist system is correct?
Choose ONE option (1 out of 4)

A. Pairwise testing is usually insufficient because most defects arise only from interactions of many parameters.
B. Pairwise testing only uses parameters directly influenced by the driver, otherwise the number of test cases becomes too large.
C. Pairwise testing can reduce testing efforts otherwise very high due to the large number of parameters.
D. Pairwise testing reduces the test suite so much that it is typically feasible within the available time.

Answer: C

Explanation:
The ISTQB CT-AI syllabus (Section4.3 - Test Design for AI-Based Systems) highlights pairwise testing as an effectivetest-case reduction techniquefor systems with many input parameters. Lane-keeping assist systems typically include environmental, sensor, and vehicle-dynamic parameters, making exhaustive testing infeasible. Pairwise testing significantly reduces the number of test cases while still capturingall 2-way interactions, which are responsible for a large proportion of software defects.
OptionBaligns with this syllabus description: pairwise testing reduces otherwise extremely large parameter combinations, making test effort manageable.
Option A overstates feasibility guarantees; the syllabus never claims pairwise testing always makes testing
"typically feasible." Option C is unsupported and incorrect because pairwise testing doesnotrestrict parameters to driver-controlled ones. Option D is incorrect because, although some defects arise from higher- order interactions, pairwise testing captures many relevant defects and is widely recognized as a pragmatic compromise.
Thus,Option Bis the correct statement.

NEW QUESTION # 17
Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system?
SELECT ONE OPTION

A. High complexity
B. Non-determinism
C. Self-learning
D. Robustness

Answer: D

Explanation:
The question asks which characteristic is least likely to cause safety-related issues for an AI system. Let's evaluate each option:
Non-determinism (A): Non-deterministic systems can produce different outcomes even with the same inputs, which can lead to unpredictable behavior and potential safety issues.
Robustness (B): Robustness refers to the ability of the system to handle errors, anomalies, and unexpected inputs gracefully. A robust system is less likely to cause safety issues because it can maintain functionality under varied conditions.
High complexity (C): High complexity in AI systems can lead to difficulties in understanding, predicting, and managing the system's behavior, which can cause safety-related issues.
Self-learning (D): Self-learning systems adapt based on new data, which can lead to unexpected changes in behavior. If not properly monitored and controlled, this can result in safety issues.
Reference:
ISTQB CT-AI Syllabus Section 2.8 on Safety and AI discusses various factors affecting the safety of AI systems, emphasizing the importance of robustness in maintaining safe operation.

NEW QUESTION # 18
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