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." (Reference: ISTQB CT-AI Syllabus v1.0, Section 4.1, page 33 of 99)

The ML workflow typically involves iterative steps, beginning with data preparation once the model and framework are selected. The syllabus explains:
"The steps shown in Figure 1 (the ML workflow) do not include the integration of the ML model with the non-ML parts of the overall system. Typically, ML models cannot be deployed in isolation and need to be integrated with the non-ML parts... The next step would be data preparation as part of the ML workflow to provide input data to support training by an ML algorithm or prediction by an ML model."

ML systems often require hardware that supports complex operations, such as matrix multiplications and other linear algebra computations. These operations are fundamental to many machine learning algorithms, particularly in deep learning. Specialist hardware (e.g., GPUs) may be used for efficiency, but complex operations are the core requirement.

Prevalence Rate and Model Performance:
The cancer detection system being developed by "Splendid Healthcare" needs to account for the fact that the type of cancer has a 2% prevalence rate in the population. This indicates that the dataset is highly imbalanced with far fewer positive (cancer) cases compared to negative (normal) cases.
Importance of Recall:
Recall, also known as sensitivity or true positive rate, measures the proportion of actual positive cases that are correctly identified by the model. In medical diagnosis, especially cancer detection, recall is critical because missing a positive case (false negative) could have severe consequences for the patient. Therefore, maximizing recall ensures that most, if not all, cancer cases are detected.
Importance of Precision:
Precision measures the proportion of predicted positive cases that are actually positive. High precision reduces the number of false positives, meaning fewer people will be incorrectly diagnosed with cancer. This is also important to avoid unnecessary anxiety and further invasive testing for those who do not have the disease.
Balancing Recall and Precision:
In scenarios where both false negatives and false positives have significant consequences, it is crucial to balance recall and precision. This balance ensures that the model is not only good at detecting positive cases but also accurate in its predictions, reducing both types of errors.
Accuracy and Specificity:
While accuracy (the proportion of total correct predictions) is important, it can be misleading in imbalanced datasets. In this case, high accuracy could simply result from the model predicting the majority class (normal) correctly. Specificity (true negative rate) is also important, but for a cancer detection system, recall and precision take precedence to ensure positive cases are correctly and accurately identified.
Conclusion:
Therefore, for a cancer detection system with a low prevalence rate, maximizing both recall and precision is crucial to ensure effective and accurate detection of cancer cases.

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.
Option A is incorrect: automation biasdoesinfluence testing, especially when testers rely excessively on AI outputs. The syllabus cautions about testers adopting the same cognitive biases as end users. Option C is incorrect because autonomous systems are not the primary context; rather,systems supporting human decisionsare most impacted. Option D is incorrect because the quality of human inputmatters significantly, and poorly designed user studies can mask or distort automation bias.
Thus,Option Bis the syllabus-accurate description of automation bias.