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| Section | Weight | Objectives |
|---|---|---|
| Testing AI-Specific Quality Characteristics | 20% | - Robustness Testing - Accuracy, Precision, Recall, F1-Score - Fairness and Bias Detection - Explainability and Interpretability |
| Neural Networks and Deep Learning | 20% | - Testing Deep Learning Models - Model Training and Evaluation Metrics - Overfitting and Underfitting - Neural Network Architecture Basics |
| AI Test Methods and Techniques | 20% | - Black-Box Testing for AI Systems - Test Oracle Techniques for AI - Adversarial Testing - Metamorphic Testing |
| Basics of AI | 8% | - Definitions and Terminology - AI Types and Techniques - Data Concepts (Training, Validation, Test Data) - Machine Learning Types (Supervised, Unsupervised, Reinforcement) |
| Testing AI-Based Systems | 20% | - Testing AI-Specific Quality Characteristics - Test Levels for AI-Based Systems - Challenges in Testing AI-Based Systems - Bias and Fairness Considerations |
| Practical Considerations | 12% | - Documentation and Reporting - Test Environment Setup for AI - Automation in AI Testing - Test Data Quality and Preparation |
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NEW QUESTION # 161
Which of the following statements regarding experience-based testing for AI-based systems is correct?
Choose ONE option (1 out of 4)
Answer: B
Explanation:
The ISTQB CT-AI syllabus explains inSection 4.4 - Experience-Based Testing for AI Systemsthat AI- based systems frequently suffer frominsufficient specifications, unpredictable model behavior, andtest oracle problems, especially when outputs depend on probabilistic or learned patterns. The syllabus explicitly states thatexploratory testingis especially valuable in such contexts because it allows testers to investigate the system interactively, observe unexpected behavior, and evaluate system responses that cannot be fully predicted beforehand. Thus, OptionCaccurately reflects the role and justification of exploratory testing for AI systems.
Option A describes data analysis rather than intuitive test design. Option B is incorrect because checklist- based testing does not dynamically adapt test cases; instead, it follows predetermined checklists. Option D incorrectly defines "tour-based testing"; tours refer to structured exploratory approaches, not biased datasets.
Therefore,Option Cis the syllabus-aligned correct statement.
NEW QUESTION # 162
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?
SELECT ONE OPTION
Answer: D
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.
:
ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing and Section 9.3 on Testing ML Models emphasize the importance of testing interactions and individual model accuracy in complex ML workflows.
NEW QUESTION # 163
Which of the following are the three activities in the data acquisition activities for data preparation?
Answer: C
Explanation:
The syllabus defines data acquisition as consisting of three steps:
"Data acquisition: The activity of acquiring data relevant to the business problem to be solved by an ML model, typically involving the activities of identifying, gathering and labelling data."
NEW QUESTION # 164
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?
Answer: D
Explanation:
Explainability in AI-based systems refers to the ease with which users can determine how the system reaches a particular result. It is a crucial aspect when demonstrating AI decision-making, as it ensures that decisions made by AI models are transparent, interpretable, and understandable by stakeholders.
Before deploying an AI-based system, a developer must validate how decisions are made in a test environment. This process falls under the characteristic of explainability because it involves clarifying how an AI model arrives at its conclusions, which helps build trust in the system and meet regulatory and ethical requirements.
* ISTQB CT-AI Syllabus (Section 2.7: Transparency, Interpretability, and Explainability)
* "Explainability is considered to be the ease with which users can determine how the AI-based system comes up with a particular result".
* "Most users are presented with AI-based systems as 'black boxes' and have little awareness of how these systems arrive at their results. This ignorance may even apply to the data scientists who built the systems. Occasionally, users may not even be aware they are interacting with an AI- based system".
* ISTQB CT-AI Syllabus (Section 8.6: Testing the Transparency, Interpretability, and Explainability of AI-based Systems)
* "Testing the explainability of AI-based systems involves verifying whether users can understand and validate AI-generated decisions. This ensures that AI systems remain accountable and do not make incomprehensible or biased decisions".
* Contrast with Other Options:
* Autonomy (B): Autonomy relates to an AI system's ability to operate independently without human oversight. While decision-making is a key function of autonomy, the focus here is on demonstrating the reasoning behind decisions, which falls under explainability rather than autonomy.
* Self-learning (C): Self-learning systems adapt based on previous data and experiences, which is different from making decisions understandable to humans.
* Non-determinism (D): AI-based systems are often probabilistic and non-deterministic, meaning they do not always produce the same output for the same input. This can make testing and validation more challenging, but it does not relate to explaining the decision-making process.
Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Since the question explicitly asks about the characteristic under which decision-making falls when being demonstrated before deployment,explainability is the correct choicebecause it ensures that AI decisions are transparent, understandable, and accountable to stakeholders.
NEW QUESTION # 165
Which statement about testing to prevent data poisoning and adversarial attacks is correct?
Choose ONE option (1 out of 4)
Answer: C
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. OptionCcorrectly reflects this syllabus definition: adversarial testing is aboutusing attacks to locate weaknesses so they can be removed or mitigated.
Option A is incorrect because regression testing does not verify data sourcing policies; it verifies unchanged functionality after modifications. Option B is incorrect because adversarial examples are oftenaddedto training datasets to improverobustness(a practice called adversarial training), not excluded. Option D is incorrect because AIB testing is not described as superior to exploratory data analysis in outlier detection; both have different purposes, and EDA remains essential for data quality assessment.
Thus,Option Cis consistent with syllabus-defined adversarial testing.
NEW QUESTION # 166
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