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ISTQB CT-AI Exam Syllabus Topics:

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

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q36-Q41):

NEW QUESTION # 36
Which of the following is an example of overfitting?

A. The model discards data it considers to be noise or outliers
B. The model is too simplistic for the data
C. The model is not able to generalize to accommodate new types of data
D. The model is missing relationships between the inputs and outputs

Answer: C

NEW QUESTION # 37
Which ONE of the following tests is LEAST likely to be performed during the ML model testing phase?
SELECT ONE OPTION

A. Testing the accuracy of the classification model.
B. Testing the speed of the training of the model.
C. Testing the speed of the prediction by the model.
D. Testing the API of the service powered by the ML model.

Answer: B

Explanation:
The question asks which test is least likely to be performed during the ML model testing phase. Let's consider each option:
Testing the accuracy of the classification model (A): Accuracy testing is a fundamental part of the ML model testing phase. It ensures that the model correctly classifies the data as intended and meets the required performance metrics.
Testing the API of the service powered by the ML model (B): Testing the API is crucial, especially if the ML model is deployed as part of a service. This ensures that the service integrates well with other systems and that the API performs as expected.
Testing the speed of the training of the model (C): This is least likely to be part of the ML model testing phase. The speed of training is more relevant during the development phase when optimizing and tuning the model. During testing, the focus is more on the model's performance and behavior rather than how quickly it was trained.
Testing the speed of the prediction by the model (D): Testing the speed of prediction is important to ensure that the model meets performance requirements in a production environment, especially for real-time applications.
Reference:
ISTQB CT-AI Syllabus Section 3.2 on ML Workflow and Section 5 on ML Functional Performance Metrics discuss the focus of testing during the model testing phase, which includes accuracy and prediction speed but not the training speed.

NEW QUESTION # 38
Before deployment of an AI-based system, a developer is expected to demonstrate in a test environment how decisions are made. Which of the following characteristics does decision making fall under?

A. Explainability
B. Non-determinism
C. Self-learning
D. Autonomy

Answer: A

Explanation:
The syllabus definesexplainabilityas the ability to understand how the AI-based system comes up with a particular result:
"Explainability is considered to be the ease with which users can determine how the AI-based system comes up with a particular result." (Reference: ISTQB CT-AI Syllabus v1.0, Section 2.7)

NEW QUESTION # 39
A local business has a mail pickup/delivery robot for their office. The robot currently uses a track to move between pickup/drop off locations. When it arrives at a destination, the robot stops to allow a human to remove or deposit mail.
The office has decided to upgrade the robot to include AI capabilities that allow the robot to perform its duties without a track, without running into obstacles, and without human intervention.
The test team is creating a list of new and previously established test objectives and acceptance criteria to be used in the testing of the robot upgrade. Which of the following test objectives will test an AI quality characteristic for this system?

A. The robot must recharge for no more than six hours a day
B. The robot must complete 99.99% of its deliveries each day
C. The robot must evolve to optimize its routing
D. The robot must record the time of each delivery which is compiled into a report

Answer: C

Explanation:
In the syllabus, theevolutioncharacteristic for AI-based systems means the ability of the system to evolve and adapt its behavior in response to changes in the environment or in its own performance:
"Evolution is the system's ability to change itself to adapt to new situations, different hardware, or a changing operational environment."

NEW QUESTION # 40
A system is to be developed to detect lung cancer using X-ray images.
Which statement BEST describes the difference between a conventional system and an AI system with supervised machine learning?
Choose ONE option (1 out of 4)

A. An AI system independently determines patterns in X-rays during training; a conventional system requires a human to program in those patterns.
B. The results of analyzing an X-ray for lung cancer using an AI system are more understandable than with a conventional system.
C. The X-ray images that an AI system can analyze must be structurally different from X-ray images used in a conventional system.
D. The implementation of an AI system consists mainly of training data, whereas that of a conventional system consists of branches and loops.

Answer: A

Explanation:
The syllabus explains the fundamental distinction betweenconventional systemsandAI-based systems using supervised machine learningin Section1.3 - AI-Based and Conventional Systems. A conventional system relies on human-programmed logic-such as branches, conditions, and explicit rules-to interpret input data.
The system behaves exactly as specified by its developers.
In contrast,AI systems using supervised learning automatically learn patternsfrom labeled data. The syllabus states that"patterns in data are used by the system to determine how it should react in the future...
The AI determines on its own what patterns or features in the data can be used". This aligns directly with Option C: an AI system identifies relevant diagnostic patterns in X-ray images during training, whereas a conventional system requires human experts to explicitly program those patterns.
Option A is incorrect because AI outputs are typicallylessexplainable, not more. Option B is incorrect because both systems can use thesame X-ray images; ML does not require structurally different images. Option D is oversimplified and not fully accurate; while training data is central to ML, AI systems also include architecture, algorithms, and preprocessing-not just data.
Thus,Option Cis the correct and syllabus-aligned answer.

NEW QUESTION # 41
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