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| Section | Weight | Objectives |
|---|---|---|
| Basics of AI | 8% | - AI Types and Techniques - Machine Learning Types (Supervised, Unsupervised, Reinforcement) - Data Concepts (Training, Validation, Test Data) - Definitions and Terminology |
| AI Test Methods and Techniques | 20% | - Black-Box Testing for AI Systems - Adversarial Testing - Test Oracle Techniques for AI - Metamorphic Testing |
| Practical Considerations | 12% | - Automation in AI Testing - Test Data Quality and Preparation - Test Environment Setup for AI - Documentation and Reporting |
| Testing AI-Based Systems | 20% | - Test Levels for AI-Based Systems - Challenges in Testing AI-Based Systems - Testing AI-Specific Quality Characteristics - Bias and Fairness Considerations |
| Neural Networks and Deep Learning | 20% | - Testing Deep Learning Models - Overfitting and Underfitting - Model Training and Evaluation Metrics - Neural Network Architecture Basics |
| Testing AI-Specific Quality Characteristics | 20% | - Explainability and Interpretability - Fairness and Bias Detection - Accuracy, Precision, Recall, F1-Score - Robustness Testing |
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NEW QUESTION # 81
Which ONE of the following types of coverage SHOULD be used if test cases need to cause each neuron to achieve both positive and negative activation values?
SELECT ONE OPTION
Answer: B
Explanation:
* Coverage for Neuron Activation Values: Sign change coverage is used to ensure that test cases cause each neuron to achieve both positive and negative activation values. This type of coverage ensures that the neurons are thoroughly tested under different activation states.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 6.2 Coverage Measures for Neural Networks, which details different types of coverage measures, including sign change coverage.
NEW QUESTION # 82
Which ONE of the following statements BEST describes a testing challenge that specifically applied to a self- learning system?
Answer: C
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 # 83
Which TWO of the below examples of systems BEST describe regression?
i. Predicting the age of a person
ii. Predicting if someone is aged over 18
iii. Predicting how much fuel is required for a journey
iv. Predicting whether a release will pass all required tests
Answer: B
Explanation:
The technically correct pair is i and iii . The option list appears to contain a transcription duplication; D is selected as the intended option for I and III . Regression is used when the ML model predicts a numeric or continuous output value . The CT-AI syllabus explicitly gives "predicting the age of a person" as an example of regression, because age is a numeric output. It also explains that regression is part of supervised learning where the required output is numeric rather than a predefined category.
Example iii, predicting how much fuel is required for a journey, is also regression because the expected result is a numeric quantity, such as litres, gallons, or kilowatt-hours. Example ii is classification because the result is a category: over 18 or not over 18. Example iv is also classification because the output is pass or fail. The key distinction is that regression predicts a numeric value, while classification assigns inputs to discrete predefined classes.
References/topics: CT-AI Syllabus Chapter 3, Section 3.1.1 "Supervised Learning"; Appendix B
"Regression."
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NEW QUESTION # 84
A data scientist is performing unsupervised learning on a set of financial records relating to previous loan applications, and trying to predict defaults on future loans. They are reporting poor functional performance because of data issues. Which ONE of the below is LEAST likely to be a contributory factor?
Answer: B
Explanation:
The correct answer is B . In unsupervised learning, the ML model is created from unlabeled data . The algorithm infers patterns in the input data and assigns inputs to groups or classes based on commonalities, rather than learning from known output labels. The CT-AI syllabus identifies clustering and association as the main unsupervised learning problem types.
Missing information about whether previous loans were granted and repaid is essentially missing outcome label information. That would be highly relevant for supervised learning, where the model learns from input data and corresponding labels, but it is least directly relevant to an unsupervised approach. By contrast, options A, C, and D are classic dataset quality issues. Missing account records indicate incomplete data; inconsistent pre-processing can create data-format and comparability defects; and irrelevant account information may adversely influence results or waste modelling resources. The syllabus explicitly lists incomplete data, data not pre-processed, and irrelevant data as typical dataset quality issues affecting ML models.
References/topics: CT-AI Syllabus Chapter 3, Section 3.1.2 "Unsupervised Learning"; Chapter 4, Section 4.3 "Dataset Quality Issues"; Chapter 4, Section 4.4 "Data Quality and its Effect on the ML Model."
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NEW QUESTION # 85
Which of the following technologies for implementing AI is considered to be a reasoning technique?
Choose ONE option (1 out of 4)
Answer: C
Explanation:
TheISTQB Certified Tester AI Testing Syllabus v1.0explicitly categorizes different AI implementation technologies in Section1.4 - AI Technologies. Within this section, AI methods are grouped into categories, one of which is"Reasoning techniques."These reasoning techniques includerule engines, deductive classifiers, case-based reasoning, and procedural reasoning. Because deductive classifiers are directly listed under this set of reasoning approaches, they are recognized as a reasoning-based AI technology.
Reasoning techniques differ from machine learning approaches because they rely onstructured, predefined rules or logicto reach conclusions. Deductive classifiers use logical inference and symbolic reasoning to classify inputs by applying encoded knowledge. This makes them fundamentally different from statistical or data-driven ML algorithms.
The other options-Linear regression,Random Forest, andGenetic algorithms-are listed by the syllabus asmachine learning techniques, not reasoning methods. Linear regression performs numerical prediction, Random Forest is an ensemble decision-tree ML model, and genetic algorithms are optimization-based ML approaches inspired by evolutionary processes. None of these involve symbolic logical deduction.
Thus, based on the authoritative definitions in the syllabus,Deductive classifiers (Option A)is the only technology classified as a reasoning technique.
NEW QUESTION # 86
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