iSQI CT-AI Exam Questions Instant Access - No Installation Required

Question 1

Which two test procedures are BEST suited for CleverPropose system testing?

Choose TWO options (2 out of 5)

ABack-to-back testing

BAdversarial testing

CMetamorphic testing

DExploratory data analysis

EPairwise testing

Answer : A, C

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.

Exploratory data analysis (Option D) is not a system testing technique. Pairwise testing (Option E) is not well suited for complex AI-based financial advice systems. Adversarial testing (Option B) is more relevant for security-critical or robustness evaluation, not primary system testing for advisory tools.

Thus,A and Care the correct and syllabus-supported choices.

Question 2

Which option describes a reasonable application of AIB testing for a self-learning system after it has changed its behavior due to user input?

Choose ONE option (1 out of 4)

AGenerating test cases for the system before and after the change, since neither has a test oracle

BComparing outputs before and after the change using different inputs

CComparing outputs before and after the change using identical inputs

DComparing outputs of a non-self-learning system with those of the changed self-learning system

Answer : C

According to Section4.6 -- AI Behaviour Testing (AIB Testing)of the ISTQB CT-AI syllabus, AIB testing is used to evaluate changes in the functional behavior of self-learning systems. The core principle iscomparing pre-change and post-change model behavior using the same test inputs, so that any difference in outputs can be attributed to the model's learning and not to differences in input data. This directly corresponds to OptionC.

Option A is incorrect because the absence of a test oracle does not justify generating new test cases; AIB relies onreusing identical inputsto detect behavioral drift. Option B is invalid because using different inputs prevents meaningful comparison. Option D is incorrect because comparing with an unrelated non-self-learning system does not allow evaluation of the same model's behavioral evolution.

Thus, OptionCaccurately represents the correct application of AIB testing: assessing model behavior changes by running identical test inputs before and after learning updates.

Question 3

Which performance metric is BEST suited to assess the quality of trained models detecting fraudulent credit card transactions?

Choose ONE option (1 out of 4)

ASensitivity

BAccuracy

CF1 value

D---

Answer : C

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.

Question 4

Which statement about using AI to analyze reported defects is MOST correct?

Choose ONE option (1 out of 4)

AML models trained with critical defect tickets can identify defects that cause serious consequences.

BML models can support duplicate defect identification when checking defect criticality.

CML models can identify categories for a reported defect during assignment.

DML models identify developers who should handle a defect based on ticket content.

Answer : C

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.

Question 5

A motorcycle engine repair shop owner wants to detect a leaking exhaust valve and fix it before it fails and causes catastrophic damage to the engine. The shop developed and trained a predictive model with historical data files from known healthy engines and ones which experienced a catastrophic failure due to exhaust valve failure. The shop evaluated 200 engines using this model and then disassembled the engines to assess the true state of the valves, recording the results in the confusion matrix below.

What is the precision of this predictive model?

A90.0%

B94.5%

C98.9%

D94.2%

Answer : B

The syllabus defines precision as:

'Precision = TP / (TP + FP) * 100%. Precision measures the proportion of positives that were correctly predicted.'

Using the confusion matrix:

TP = 90

FP = 10Thus: Precision = (90 / (90 + 10)) * 100% = 90 / 100 * 100% = 90%However, the confusion matrix totals suggest that the calculation should be done in the form:Precision = 90 / (90 + 10) * 100% = 90%Since the given answers do not include exactly 90%, the closest approximation and the correct answer, as described in the syllabus, would be 90%.(Reference: ISTQB CT-AI Syllabus v1.0, Section 5.1, page 40 of 99)

Question 6

When verifying that an autonomous AI-based system is acting appropriately, which of the following are MOST important to include?

ATest cases to verify that the system automatically confirms the correct classification of training data

BTest cases to detect the system appropriately automating its data input

CTest cases to detect the system prompting for unnecessary human intervention

DTest cases to verify that the system automatically suppresses invalid output data

Answer : C

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)

Question 7

There is a growing backlog of unresolved defects for your project. You know the developers have an ML model that they have created which has learned which developers work on which type of software and the speed with which they resolve issues. How could you use this model to help reduce the backlog and implement more efficient defect resolution?

AUse it to prioritize defects automatically based on the time expected for the fix to be made, the speed of the fix, and the likelihood of regressions

BUse it to assign defects to the best developer to resolve the problem and to load balance the defect assignments among the developers

CUse it to determine the root cause of each defect and develop a process improvement plan that can be implemented to remove the most common root causes

DUse it to review the code and determine where more defects are likely to occur so that testing can be targeted to those areas

Answer : B

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)

iSQI Certified Tester AI Testing CT-AI Exam Questions