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| Section | Weight | Objectives |
|---|---|---|
| Practical Considerations | 12% | - Test Data Quality and Preparation - Automation in AI Testing - Test Environment Setup for AI - Documentation and Reporting |
| AI Test Methods and Techniques | 20% | - Black-Box Testing for AI Systems - Adversarial Testing - Metamorphic Testing - Test Oracle Techniques for AI |
| Neural Networks and Deep Learning | 20% | - Model Training and Evaluation Metrics - Overfitting and Underfitting - Testing Deep Learning Models - Neural Network Architecture Basics |
| Basics of AI | 8% | - Definitions and Terminology - Data Concepts (Training, Validation, Test Data) - AI Types and Techniques - Machine Learning Types (Supervised, Unsupervised, Reinforcement) |
| Testing AI-Based Systems | 20% | - Testing AI-Specific Quality Characteristics - Bias and Fairness Considerations - Challenges in Testing AI-Based Systems - Test Levels for AI-Based Systems |
| Testing AI-Specific Quality Characteristics | 20% | - Robustness Testing - Fairness and Bias Detection - Explainability and Interpretability - Accuracy, Precision, Recall, F1-Score |
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NEW QUESTION # 150
Which ONE of the following statements is MOST true about black-box adversarial testing?
Answer: C
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 ONE of the following statements BEST describes a testing challenge that specifically applied to a self- learning system?
Answer: D
Explanation:
The correct answer is A . A self-learning system can modify its own behaviour as it learns from operational data, environmental inputs, or its own decisions. The CT-AI syllabus identifies "unexpected change" as a major testing challenge for self-learning systems. It explains that when systems change themselves and their outputs, the results of previously passing tests can change, creating a regression testing and test design challenge. Testers may need tests that remain valid as the system evolves, and may also need new tests based on newly observed behaviours.
Option B relates more directly to autonomy , where the system must know when to return control to a human operator. Option C relates to testing for inappropriate bias, particularly when hidden or inferred attributes may influence outcomes. Option D relates to explainability, where the link between an input and a model output may be difficult to understand. These are important AI testing issues, but they are not the most specific self- learning challenge.
References/topics: CT-AI Syllabus Chapter 8, Section 8.1 "Challenges Testing Self-Learning Systems."
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NEW QUESTION # 152
Which statement about AI-based test case generation is correct?
Choose ONE option (1 out of 4)
Answer: C
Explanation:
The ISTQB CT-AI syllabus indicates inSection 5.2 - AI for Testingthat AI-generated test cases may not come withpredefined expected results. This is because test-case generation methods-such as evolutionary algorithms, reinforcement learning, or clustering-based sampling-produceinputs, but the tester must still determine the correct outputs. Therefore, OptionCis correct: expected results may not be available, especially when AI produces novel or previously unseen input combinations.
Option A is incorrect: AI-based test generation can improve coverage by exploring large input spaces. Option B is incorrect because the need for oracles does not depend on whether the test case is AI-generated. Option D is incorrect because the syllabus allows using a model as an oracle inback-to-back testing, provided limitations are understood.
Therefore,Option Cis the correct syllabus-aligned statement.
NEW QUESTION # 153
Which statement regarding AI for defect prediction is correct?
Answer: A
Explanation:
Section5.3 - AI Support for Defect Predictionof the ISTQB CT-AI syllabus explains that AI-based defect prediction models rely onhistorical patterns, including past defects, code behavior, and similar system configurations. ML models trained on prior defect data can identifycomponents likely to contain defectswhen new changes resemble previous defect-inducing patterns. This directly supports Option A, which states that defect prediction is most effective when based on previous similar constellations.
NEW QUESTION # 154
A system was developed for screening the X-rays of patients for potential malignancy detection (skin cancer). A workflow system has been developed to screen multiple cancers by using several individually trained ML models chained together in the workflow. Testing the pipeline could involve multiple kind of tests (I - III):
I . Pairwise testing of combinations
II . Testing each individual model for accuracy
III . A/B testing of different sequences of models
Which ONE of the following options contains the kinds of tests that would be MOST APPROPRIATE to include in the strategy for optimal detection?
Answer: C
Explanation:
The question asks which combination of tests would be most appropriate to include in the strategy for optimal detection in a workflow system using multiple ML models.
Pairwise testing of combinations (I): This method is useful for testing interactions between different components in the workflow to ensure they work well together, identifying potential issues in the integration.
Testing each individual model for accuracy (II): Ensuring that each model in the workflow performs accurately on its own is crucial before integrating them into a combined workflow.
A/B testing of different sequences of models (III): This involves comparing different sequences to determine which configuration yields the best results. While useful, it might not be as fundamental as pairwise and individual accuracy testing in the initial stages.
NEW QUESTION # 155
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