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ISTQB CT-AI Exam Syllabus Topics:

Section	Objectives
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
AI Quality Characteristics	- Quality attributes 1. Accuracy and robustness 2. Explainability and transparency
AI System Lifecycle and Operations	- Deployment and monitoring 1. Post-deployment monitoring 2. Model deployment strategies - Continuous learning systems 1. Retraining strategies 2. Model drift detection
Machine Learning Fundamentals for Testing	- ML lifecycle 1. Training, validation, and evaluation 2. Data collection and preparation - Model types 1. Deep learning basics 2. Supervised and unsupervised learning
Data Quality and Bias	- Data quality assurance 1. Data labeling quality 2. Data completeness and consistency - Bias and fairness 1. Types of bias in AI systems 2. Fairness testing approaches
Ethics and Risk in AI Testing	- Ethical considerations 1. Privacy and security concerns 2. Responsible AI principles - Risk-based testing for AI 1. Mitigation strategies 2. Risk identification in AI systems
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

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q149-Q154):

NEW QUESTION # 149
A transportation company operates three types of delivery vehicles in its fleet. The vehicles operate at different speeds (slow, medium, and fast). The transportation company is attempting to optimize scheduling and has created an AI-based program to plan routes for its vehicles using records from the medium-speed vehicle traveling to selected destinations. The test team uses this data in metamorphic testing to test the accuracy of the estimated travel times created by the AI route planner with the actual routes and times.
Which of the following describes the next phase of metamorphic testing?

A. The team decomposes each route into the relevant components that affect the travel time, such as traffic density and vehicle power. The team then uses statistical analysis to characterize the influence of each component to calculate the fast and slow vehicle route times.
B. The team uses the same AI route planner to create routes that are longer and shorter but follow the same track. Finally, by driving the fast vehicles on the long routes and slow vehicles on the short routes and vice versa, the AI system will have enough information to infer travel times for all vehicles on all routes.
C. The team tests the time required for the fast and slow vehicles to travel the same route as the medium vehicle. Then, by calculating the speed difference, they then predict how much faster or slower the vehicles will travel. That information is then used to verify that the arrival time of the vehicles meets the expected result.
D. The team uses an AI system to select the most dissimilar routes. With this information, any of the AI routes can be metaphorically transformed into a fast or slow route.

Answer: C

Explanation:
The syllabus describes metamorphic testing as:
"Testing involves defining metamorphic relations and then applying those relations to check that the transformations result in expected outcomes, even when the expected output of the system is unknown or not well-defined." In this scenario, applying the metamorphic relation (speed differences) and checking the transformed outcome (arrival times) fits the definition of metamorphic testing.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 9.5, page 69 of 99)

NEW QUESTION # 150
Which ONE of the following statements is MOST true about black-box adversarial testing?

A. The training data is manipulated
B. The approach relies on the transferability of the attacks
C. Combinatorial testing techniques are used
D. The algorithm used for the model is known to the tester

Answer: B

Explanation:
In black-box adversarial testing, the tester does not have access to the internal workings of the model (such as the algorithm or the training data). The approach relies on the transferability of the attacks, where adversarial examples generated for one model are used to test the robustness of another model, even without direct access to its internal details.

NEW QUESTION # 151
Which of the following decisions is BEST as a test approach for the described situation?

A. You plan experience-based testing by the entire team at the system test level to ensure that the end users are satisfied.
B. You plan to perform reviews and exploratory data analysis of the image data sets to reduce the risk of a lack of representativeness of this data.
C. You execute the test cases from the old camera model at the integration test level; no further dynamic tests of the operating data pipeline are necessary.
D. You plan to manually execute regular regression tests of the new camera function, particularly for system tests.

Answer: A

Explanation:
The ISTQB CT-AI syllabus emphasizes that testing AI-based systems require scross-functional collaboration and experience-based testingwhen parts of the team lack domain knowledge. In this scenario, the ML expert understands ML and dataset preparation but lacks knowledge ofcamera system behavior, the device's operational data pipeline, and end-user workflows. The remainder of the team understands the domain and system testing but not ML. Section4.4 - Human Factors and AI Testingand4.3 - System Testing of AI Components highlight that when domain understanding is unevenly distributed,experience-based testing conducted by the full team(testers, developers, domain experts) is the most effective approach. This ensures that AI outputs align with actual user expectations and system behavior. Option C aligns exactly with this principle.

NEW QUESTION # 152
Which ONE of the following options describes a scenario of A/B testing the LEAST?
SELECT ONE OPTION

A. A comparison of the performance of an ML system on two different input datasets.
B. A comparison of the performance of two different ML implementations on the same input data.
C. A comparison of two different offers in a recommendation system to decide on the more effective offer for same users.
D. A comparison of two different websites for the same company to observe from a user acceptance perspective.

Answer: A

Explanation:
A/B testing, also known as split testing, is a method used to compare two versions of a product or system to determine which one performs better. It is widely used in web development, marketing, and machine learning to optimize user experiences and model performance. Here's why option C is the least descriptive of an A/B testing scenario:
* Understanding A/B Testing:
* In A/B testing, two versions (A and B) of a system or feature are tested against each other. The objective is to measure which version performs better based on predefined metrics such as user engagement, conversion rates, or other performance indicators.
* Application in Machine Learning:
* In ML systems, A/B testing might involve comparing two different models, algorithms, or system configurations on the same set of data to observe which yields better results.
* Why Option C is the Least Descriptive:
* Option C describes comparing the performance of an ML system on two different input datasets.
This scenario focuses on the input data variation rather than the comparison of system versions or features, which is the essence of A/B testing. A/B testing typically involves a controlled experiment with two versions being tested under the same conditions, not different datasets.
* Clarifying the Other Options:
* A. A comparison of two different websites for the same company to observe from a user acceptance perspective: This is a classic example of A/B testing where two versions of a website are compared.
* B. A comparison of two different offers in a recommendation system to decide on the more effective offer for the same users: This is another example of A/B testing in a recommendation system.
* D. A comparison of the performance of two different ML implementations on the same input data: This fits the A/B testing model where two implementations are compared under the same conditions.
References:
* ISTQB CT-AI Syllabus, Section 9.4, A/B Testing, explains the methodology and application of A/B testing in various contexts.
* "Understanding A/B Testing" (ISTQB CT-AI Syllabus).

NEW QUESTION # 153
Which ONE of the following statements BEST describes a testing challenge that specifically applied to a self-learning system?

A. In can be difficult to explain the link between test inputs and outputs
B. External data sources might be required to ensure that the system is unbiased
C. It is necessary to test whether the system will relinquish control to a human at the right time
D. When systems change themselves the results of previously passing tests may change

Answer: D

Explanation:
A key challenge in testing self-learning systems is that, as the system learns and adapts over time, the results of previously passing tests may change. This is because the system's behavior evolves as it learns from new data, potentially altering how it responds to test inputs. This dynamic nature of self-learning systems makes it challenging to maintain consistent and reliable test results.

NEW QUESTION # 154
......

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