Free Practice Questions for ISTQB CT-AI Exam

The syllabus states that non-determinism is one of the key challenges for ensuring safety in AI-based systems:

"The characteristics of AI-based systems that make it more difficult to ensure they are safe... include: complexity, non-determinism, probabilistic nature, self-learning, lack of transparency, interpretability and explainability, and lack of robustness."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 2.8, page 25 of 99)

Clustering is a type of machine learning technique used to group similar data points into clusters. It is a key concept in unsupervised learning, where the algorithm tries to find patterns or groupings in data without prior knowledge of output classes. Let's analyze each option:

A. Clustering is classification of a continuous quantity.

This is incorrect. Classification typically involves discrete categories, whereas clustering involves grouping similar data points. Classification of continuous quantities is generally referred to as regression.

B. Clustering is supervised learning.

This is incorrect. Clustering is an unsupervised learning technique because it does not rely on labeled data.

C. Clustering is done without prior knowledge of output classes.

This is correct. In clustering, the algorithm groups data points into clusters without any prior knowledge of the classes. It discovers the inherent structure in the data.

D. Clustering requires you to know the classes.

This is incorrect. Clustering does not require prior knowledge of classes. Instead, it aims to identify and form the classes or groups based on the data itself.

Therefore, the correct answer isCbecause clustering is an unsupervised learning technique done without prior knowledge of output classes​​.

The question asks which deficiency is most likely to be discovered by the test expert given the scenario of poor real-world performance despite good isolated accuracy.

A lack of similarity between the training and testing data (A): This is a common issue in ML where the model performs well on training data but poorly on real-world data due to a lack of representativeness in the training data. This leads to poor generalization to new, unseen data.

The input data has not been tested for quality prior to use for testing (B): While data quality is important, this option is less likely to be the primary reason for the described issue compared to the representativeness of training data.

A lack of focus on choosing the right functional-performance metrics (C): Proper metrics are crucial, but the issue described seems more related to the data mismatch rather than metric selection.

A lack of focus on non-functional requirements testing (D): Non-functional requirements are important, but the scenario specifically mentions issues with detecting real cancer cases, pointing more towards data issues.

 Challenges for Acquiring Test Data in ML Systems:Compliance needs, the changing nature of data over time, and sourcing data from public sources are significant challenges. Data being generated quickly is generally not a challenge; it can actually be beneficial as it provides more data for training and testing.

 Reference:ISTQB_CT-AI_Syllabus_v1.0, Sections on Data Preparation and Data Quality Issues​​​​.

Section2.2 – Data Preparationand4.1 – Challenges in Testing AI-Based Systemsdescribe difficulties in obtaining and managing large, representative datasets. AI-based systems requirerealistic, diverse, and representativedata reflecting real-world variations. The syllabus emphasizes that assembling such datasets is time-consuming, resource-intensive, and often constrained by availability, privacy, or domain complexity. Option B directly corresponds to these documented challenges.

Option A is incorrect: using the same implementation risksdefect masking, not preventing it; the syllabus warns against this practice. Option C is incorrect because real-world data naturally evolves, and the syllabus notes thatdriftis normal; expecting stable input data contradicts operational reality. Option D is incorrect: although data privacy is important, the syllabus does not claim that artificially generated data always requires legal approval, nor that sanitization/encryption is mandatory for synthetic data.

Thus,Option Baccurately reflects syllabus-defined difficulties in producing representative test data.

The syllabus highlights that testing for unnecessary human intervention is a key focus for autonomous AI-based systems:

"For autonomous AI-based systems, testers must ensure that the system does not prompt for unnecessary human intervention, as this contradicts the autonomy concept."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 8.2, page 59 of 99)