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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 2	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 3	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 4	Machine Learning ML: This section includes the classification and regression as part of supervised learning, explaining the factors involved in the selection of ML algorithms, and demonstrating underfitting and overfitting.
Topic 5	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 6	ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.
Topic 7	systems from those required for conventional systems.
Topic 8	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 9	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 10	ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q92-Q97):

NEW QUESTION # 92
A word processing company is developing an automatic text correction tool. A machine learning algorithm was used to develop the auto text correction feature. The testers have discovered that when they start typing
"Isle of Wight" it fills in "Isle of Eight". Several UAT testers have accepted this change without noticing.
What type of bias is this?

A. Ignorance/Cognitive
B. Complacency/Disregard
C. Automation/Complacency
D. Geographical/Locality

Answer: C

Explanation:
The syllabus describes automation bias as:
"A type of bias caused by a person favoring the recommendations of an automated decision-making system over other sources." This is also known as complacency bias, where testers accept automated system outputs without questioning them.
(Reference: ISTQB CT-AI Syllabus v1.0, Glossary, Page 89 of 99)

NEW QUESTION # 93
Which option gives the correct values for accuracy and precision from the confusion matrix?
Choose ONE option (1 out of 4)

A. Accuracy = 80%, Precision = 75%
B. Accuracy = 80%, Precision = 50%
C. Accuracy = 50%, Precision = 75%
D. Accuracy = 75%, Precision = 80%

Answer: A

Explanation:
From the confusion matrix:
* True Positives (TP) = 15
* False Positives (FP) = 5
* False Negatives (FN) = 15
* True Negatives (TN) = 65
Accuracy= (TP + TN) / Total
= (15 + 65) / 100
=80%
Precision= TP / (TP + FP)
= 15 / (15 + 5)
= 15 / 20
=75%
Section3.2 - Functional Performance Criteriain the syllabus explains accuracy and precision exactly these ways when evaluating ML classification performance.
Option B is therefore the only correct pair of values.

NEW QUESTION # 94
Which option describes a reasonable application of AIB testing for a self-learning system after it has changed its behavior due to user input?

A. Comparing outputs before and after the change using different inputs
B. Comparing outputs of a non-self-learning system with those of the changed self-learning system
C. Generating test cases for the system before and after the change, since neither has a test oracle
D. Comparing outputs before and after the change using identical inputs

Answer: D

Explanation:
According to Section4.6 - AI Behaviour Testing (AIB Testing)of the ISTQB CT-AI syllabus, AIB testing is used to evaluate changes in the functional behavior of self-learning systems. The core principle iscomparing pre-change and post-change model behavior using the same test inputs, so that any difference in outputs can be attributed to the model's learning and not to differences in input data. This directly corresponds to Option C.

NEW QUESTION # 95
Which ONE of the following options does NOT describe an Al technology related characteristic which differentiates Al test environments from other test environments?

A. Challenges in the creation of scenarios of human handover for autonomous systems.
B. The challenge of mimicking undefined scenarios generated due to self-learning
C. The challenge of providing explainability to the decisions made by the system.
D. Challenges resulting from low accuracy of the models.

Answer: A

Explanation:
AI test environments have several unique characteristics that differentiate them from traditional test environments. Let's evaluate each option:
A). Challenges resulting from low accuracy of the models.
Low accuracy is a common challenge in AI systems, especially during initial development and training phases. Ensuring the model performs accurately in varied and unpredictable scenarios is a critical aspect of AI testing.
B). The challenge of mimicking undefined scenarios generated due to self-learning.
AI systems, particularly those that involve machine learning, can generate undefined or unexpected scenarios due to their self-learning capabilities. Mimicking and testing these scenarios is a unique challenge in AI environments.
C). The challenge of providing explainability to the decisions made by the system.
Explainability, or the ability to understand and articulate how an AI system arrives at its decisions, is a significant and unique challenge in AI testing. This is crucial for trust and transparency in AI systems.
D). Challenges in the creation of scenarios of human handover for autonomous systems.
While important, the creation of scenarios for human handover in autonomous systems is not a characteristic unique to AI test environments. It is more related to the operational and deployment challenges of autonomous systems rather than the intrinsic technology-related characteristics of AI .
Given the above points, option D is the correct answer because it describes a challenge related to operational deployment rather than a technology-related characteristic unique to AI test environments.

NEW QUESTION # 96
Which statement about testing to prevent data poisoning and adversarial attacks is correct?

A. Adversarial testing consists of using adversarial attacks to identify vulnerabilities so that they can be eliminated.
B. Using AIB testing to identify data poisoning can better identify outliers than exploratory data analysis.
C. Regression testing can be used to verify data sourcing policies to ensure the source of training data.
D. The adversarial examples identified during adversarial testing must not be added to the training data so that they do not poison the model.

Answer: A

Explanation:
The ISTQB CT-AI syllabus explains inSection 4.5 - Testing AI-Specific Risksthat adversarial testing is a structured test activity in which testers applyadversarial attacks--crafted or perturbed inputs--to intentionally expose weaknesses in the ML model. The purpose is to identify vulnerabilities that could be exploited throughdata poisoning,evasion attacks, orinput manipulation. Option C correctly reflects this syllabus definition: adversarial testing is aboutusing attacks to locate weaknesses so they can be removed or mitigated.

NEW QUESTION # 97
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