Free Practice Questions for ISTQB CT-AI Exam

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 explains the fundamental distinction betweenconventional systemsandAI-based systems using supervised machine learningin Section1.3 – AI-Based and Conventional Systems. A conventional system relies on human-programmed logic—such as branches, conditions, and explicit rules—to interpret input data. The system behaves exactly as specified by its developers.

In contrast,AI systems using supervised learning automatically learn patternsfrom labeled data. The syllabus states that“patterns in data are used by the system to determine how it should react in the future… The AI determines on its own what patterns or features in the data can be used”. This aligns directly with Option C: an AI system identifies relevant diagnostic patterns in X-ray images during training, whereas a conventional system requires human experts to explicitly program those patterns.

Option A is incorrect because AI outputs are typicallylessexplainable, not more. Option B is incorrect because both systems can use thesame X-ray images; ML does not require structurally different images. Option D is oversimplified and not fully accurate; while training data is central to ML, AI systems also include architecture, algorithms, and preprocessing—not just data.

Thus,Option Cis the correct and syllabus-aligned answer.

The ISTQB CT-AI syllabus (Section5.3 – AI Support for Defect Analysis) explains that AI can categorize defect reports using natural language processing or classification models. Categorization helps route defects efficiently and determine which areas of the system are affected. Thus, OptionCis correct: AI canidentify defect categories, supporting assignment and triage.

Option A is incorrect because ML cannot infer severity or consequence without domain-specific risk modeling. Option B inaccurately ties duplicate detection to criticality; the syllabus separatesduplicate detectionfromseverity analysis. Option D is incorrect because AI may assist in routing defects using categories, but the syllabus does not claim AI identifies specific developers.

Thus,Option Cis the syllabus-accurate statement.

The syllabus states:

“When testing probabilistic and non-deterministic systems, the same input may produce different outputs. Tests need to be run several times to produce statistically valid test results, ensuring that an appropriate number of answers are accurate.”

(Reference: ISTQB CT-AI Syllabus v1.0, Section 8.4, page 58 of 99)

Bias detection at thedata levelis performed by reviewingdata acquisition and preprocessing steps, as explained in Section2.3 – Data Quality and Biasof the ISTQB CT-AI syllabus. Sample bias arises when data is distorted or when preprocessing introduces unintended shifts—for example, by filtering, normalization, or labeling steps that disproportionately affect subsets of the data. OptionBcorrectly reflects this: reviewers can identify whether preprocessing steps have altered the dataset in a way that introducessample distortions. This aligns perfectly with syllabus guidance on reviewing data pipelines for bias sources.

Option A is incorrect because algorithmic bias originates from themodel, not data collection procedures. Option C is incorrect because LIME is anexplainabilitymethod applied post-model, not in data reviews. Option D incorrectly states “algorithmic bias,” but preprocessing affectssample bias, not algorithmic bias.

Thus, OptionBcorrectly matches the syllabus’ definition of how bias can be detected during data-related reviews.

The ISTQB CT-AI syllabus explains in Section3.2 – Functional Performance Criteria of ML Modelsthat accuracy becomes unreliable whenclass imbalanceexists. In fraud detection, more than 99% of transactions are non-fraudulent, meaning the dataset is extremely imbalanced. Because accuracy counts all correct non-fraudulent classifications, it will appear artificially high, even if the fraud detection performance is poor. Therefore, accuracy is not suitable for evaluating fraud detection systems.

The syllabus further explains thatsensitivity (recall)captures the proportion of correctly identified fraudulent cases. This metric is important, as missing fraudulent events can cause high financial loss. However, the client also stresses thatlegitimate transactions must be correctly identified, meaningfalse positives must be minimizedto maintain customer satisfaction.

TheF1 score, defined as the harmonic mean ofprecision and recall, balances both:

Precision protects legitimate customers by minimizing false alarms.

Recall ensures fraudulent transactions are detected.

Section 3.2 emphasizes that when both false positives and false negatives have significant consequences, and the data is highly imbalanced,F1 is the most appropriate metricbecause it reflects the combined importance of detecting fraud while avoiding unnecessary alerts. Thus,Option Cis the correct choice.

Detecting biases in the ML pipeline involves various tests to ensure fairness and accuracy throughout the ML process.

Testing the distribution shift in the training data for inappropriate bias (A): This involves checking if there is any shift in the data distribution that could lead to bias in the model. It is an important test but not the most direct method for detecting biases.

Test the model during model evaluation for data bias (B): This is a critical stage where the model is evaluated to detect any biases in the data it was trained on. It directly addresses potential data biases in the model.

Testing the data pipeline for any sources for algorithmic bias (C): This test is crucial as it helps identify biases that may originate from the data processing and transformation stages within the pipeline. Detecting sources of algorithmic bias ensures that the model does not inherit biases from these processes.

Check the input test data for potential sample bias (D): While this is an important step, it focuses more on the input data and less on the overall data pipeline.

Hence, the most likely useful test to help detect different kinds of biases in the ML pipeline isB. Test the model during model evaluation for data bias.

The ISTQB CT-AI syllabus explains that reusing pre-trained models is strongly related tosimilarity between the original task and the new task. Section1.8 – Pre-trained Models and Transfer Learningstates that reuse is effective when the new task is similar to the original one, such as adapting a cat-classifier to classify dog breeds. The syllabus warns about risks related toinput differences,data preparation inconsistencies,inherited shortcomings, andexplainability issues. These are legitimate objections (matching options A, B, and C) because large differences in image inputs or patterns can undermine transfer learning; misclassification risk can increase; and explainability often decreases when reusing pre-trained models .

However,output differences are NOT a valid concernhere. Both the leaf-based and flower-based ML models classifythe same plant species, meaning theiroutputs are identical. The syllabus does not identify output mismatch as a transfer-learning risk. Real risks concerninputs,bias inheritance,model transparency, andtraining differences—not output labels. Therefore, OptionDdescribes an objection that can be safelyneglected, because output classes are the same and do not hinder reuse.

The syllabus mentions that AI can help optimize regression test suites:

"An AI-based tool can perform optimization of the regression test suite by analyzing… the information from previous test results, associated defects, and the latest changes that have been made, such as features which are broken more frequently and which tests exercise code impacted by recent changes."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 11.4, page 79 of 99)

 Testing Autonomy:Testing for human handover when it should not be relinquishing control is the least appropriate because it contradicts the very definition of autonomous systems. The other tests are relevant to ensuring smooth operation and transitions between human and AI control.

 Reference:ISTQB_CT-AI_Syllabus_v1.0, Sections on Testing Autonomous AI-Based Systems and Testing for Human-AI Interaction​​​​. ​