Free ISTQB CT-AI Practice Exam with Questions & Answers | Set: 3

The ISTQB CT-AI syllabus states in Section2.10 – Explainability, Transparency, and Interpretabilitythat interpretability isuser-dependent, meaning different users understand explanations differently. This is because interpretability depends not only on the ML algorithm but also on the user’s domain knowledge, experience, and expectations. OptionBdirectly reflects this syllabus principle: interpretability testing must consideruser background, and explanations must be comprehensible to the intended user group.

Option A is incorrect because explainability testing requires substantial information about the model, data, and expected behavior—not just exploratory effort. Option C is incorrect because explainability isnotgenerally quantifiable through dynamic testing alone, and the syllabus does not assert model-type specificity in this way. Option D exaggerates LIME’s capabilities. LIME offers approximate local explanations, but cannotpreciselystate root causes; the syllabus emphasizes itslimitationsand that explanations areapproximations, not exact reasons.

Therefore,Option Bis the most syllabus-aligned and correct statement.

According to the syllabus, pre-trained models often inherit biases and limitations from the data and processes used in their original training, which may not align with the new use case. Specifically, the syllabus states:

"When using a pre-trained model, the training data and process cannot be fully controlled or known by the user of the model. As a result, the model can inherit biases or inaccuracies that were part of its original development and training process."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 1.8.3)

The syllabus explains that ML models can be used to analyze reported defects and suggest which developers are best suited to fix them based on historical data about defect assignment and resolution speed:

"Assignment: ML models can suggest which developers are best suited to fix particular defects, based on the defect content and previous developer assignments."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 11.2, page 78 of 99)

The syllabus defines back-to-back testing as a method to compare a modified AI system to the previous version, which is ideal in this scenario:

"Back-to-back testing is performed by comparing the outputs of two systems that are supposed to provide the same outputs, one being a known and trusted system and the other being the test system. This approach can be used to test ML systems after re-training to verify that improvements have not introduced regressions."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 9.3, page 67 of 99)

The syllabus defines reward hacking as:

"Reward hacking can result from an AI-based system achieving a specified goal by using a 'clever' or 'easy' solution that perverts the spirit of the designer’s intent."

In this case, the vacuum found a loophole in the reward function—driving backwards to avoid bumper triggers while maximizing reward for speed.

(Reference: ISTQB CT-AI Syllabus v1.0, Section 2.6, page 24 of 99)

The syllabus discusses using AI-based tools to reduce GUI test brittleness:

"AI can be used to reduce the brittleness of this approach, by employing AI-based tools to identify the correct objects using various criteria (e.g., XPath, label, id, class, X/Y coordinates), and to choose the historically most stable identification criteria."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 11.6.1)

The syllabus defines overfitting as:

“Overfitting is when the ML model learns the training data so well that it is unable to generalize to accommodate new data.”

This occurs when the model memorizes the training data, including noise, instead of learning the general patterns.

(Reference: ISTQB CT-AI Syllabus v1.0, Section 3.5.1, page 31 of 99)

Recognizing digits from a scan of handwritten numbers using machine learning is an example of classification. Here’s a breakdown:

Classification: This type of machine learning involves categorizing input data into predefined classes. In this scenario, the input data (handwritten digits) are classified into one of the 10 digit classes (0-9).

Why Not Other Options:

Reinforcement Learning: This involves learning by interacting with an environment to achieve a goal, which does not fit the problem of recognizing digits.

Regression: This is used for predicting continuous values, not discrete categories like digit recognition.

Clustering: This involves grouping similar data points together without predefined classes, which is not the case here.

Section1.7 – Neural Networksof the ISTQB CT-AI syllabus explains that neural networks consist of neurons connected by weighted links. During training,learning occurs by adjusting the weights on these connections. This is the essence of gradient descent and backpropagation. Option B correctly states this behavior: only theweightsare modified, not the activation functions, neuron counts, or architectural structure.

Option A is incorrect because a neuron’sbiasis not determined by previous activations; it is an independent trainable parameter added to the weighted input sum. Option C is incorrect because the syllabus states that asingle-layer perceptron is a valid type of neural network, although limited to linearly separable problems. Option D is incorrect because no rule requires the number of input neurons to exceed or equal the number of output neurons. Instead, input neurons correspond to thenumber of features, while output neurons correspond totasks or classes.

Therefore,Option Bprecisely reflects the syllabus definition of what changes during neural network training.

Section2.4 – Data Labeling Approachesof the ISTQB CT-AI syllabus explicitly definesAI-assisted data labelingas a hybrid process in which an automated tool performs the initial labeling and human annotators subsequently verify, correct, or complete the labels. This two-step process improves efficiency while retaining human oversight to ensure data quality. The syllabus describes this method as an effective compromise when manual labeling alone would be too slow or costly, and when initial automation can identify obvious patterns before a human provides the final authoritative labels .

Option A (internal labeling) refers to labeling conducted by the organization’s own staff but does not imply automation. Option B (crowdsourced labeling) leverages a distributed workforce, typically without automation. Option C (outsourced labeling) transfers labeling tasks to external vendors but similarly does not involve an AI-first step. Only OptionDreflects the two-stage automated-then-human workflow described in the syllabus.

Therefore,AI-assisted data labeling(Option D) is unequivocally correct.