Automation bias is defined in Section4.4 - Human Factors in AI Testingof the ISTQB CT-AI syllabus. It refers to the human tendency to overly trust, rely on, or defer to automated system outputs. The syllabus explains that this bias arises especially indecision-support systems, where humans may accept AI judgments without adequate verification. This aligns directly with Option B).

The ISTQB CT-AI syllabus explains that AI-based decision-support systems benefit strongly fromback-to-back testingandmetamorphic testingwhen oracle problems exist or when limited regression tests are available. In this scenario, CleverPropose replaces an older advisory system.Back-to-back testing(Option A) is ideal because the outputs of the existing conventional system can serve as areference, enabling comparison against the new AI system. This is exactly what the syllabus recommends when AI is replacing a traditional deterministic system.
Metamorphic testing(Option C) is also appropriate, as stated in Section4.6 - Metamorphic Relations. With limited regression tests and complex decision logic, testers can define metamorphic relations such as "if customer income increases, risk rating should not worsen." These relations allow validation even when exact expected outputs are unavailable.

Accuracy = (True Positives + True Negatives) / (Total Samples)
In this case:
- True Positives (TP) = 60
- True Negatives (TN) = 11
- False Positives (FP) = 20
- False Negatives (FN) = 9
Accuracy = (60 + 11) / (60 + 11 + 20 + 9) * 100% = 71%

The AI Effectis clearly defined in the ISTQB Certified Tester AI Testing Syllabus v1.0 under Section1.1 - Definition of AI and AI Effect. The document explains that society's understanding of what qualifies as "AI" changes over time. Technologies once considered AI--such as expert systems from the 1970s and 1980s or early chess-playing systems--are no longer viewed as AI today. This phenomenon is explicitly labeled the"AI Effect,"described as"the changing perception of what constitutes AI ."The syllabus states that as AI capabilities become routine or widely implemented, they often stop being perceived as true artificial intelligence .

The ISTQB CT-AI syllabus (Section3.2 - Model Evaluation) specifies the correct usage oftraining, validation, andtestdatasets. It emphasizes that the test dataset must be representative of the real operational dataand must beequivalent in distribution to the training and validation sets, ensuring a fair and unbiased evaluation. Option D precisely matches this requirement.