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| Certification Vendor: | ISTQB |
|---|---|
| Exam Name: | ISTQB Certified Tester - AI Testing |
| Exam Number: | CT-AI |
| Passing Score: | 65% |
| Exam Price: | EUR 250 |
| Exam Duration: | 60 minutes |
| Certificate Validity Period: | Lifetime (no expiration) |
| Exam Format: | Multiple Choice |
| Real Exam Qty: | 40 |
| Available Languages: | English |
| Related Certifications: | ISTQB CTFL ISTQB CTAL-TTA |
| Sample Questions: | ISTQB CT-AI Sample Questions |
| Exam Way: | Online proctored or in-person at authorized testing centers |
| Pre Condition: | ISTQB CTFL (Certified Tester Foundation Level) certification is recommended but not mandatory |
| Official Syllabus URL: | https://www.istqb.org/certifications/artificial-intelligence-testing-certification |
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NEW QUESTION # 128
You are developing a "flower" ML model... Which of the following describes an objection that you can NEGLECT in your risk assessment?
Answer: D
Explanation:
The ISTQB CT-AI syllabus explains that reusing pre-trained models is strongly related to similarity 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, and explain ability 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 classify the 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, Option D describes an objection that can be safely neglected, because output classes are the same and do not hinder reuse.
NEW QUESTION # 129
Which of the following aspects is a challenge when handling test data for an AI-based system?
Answer: A
Explanation:
The syllabus explicitly mentions challenges of handling personal data and ensuring privacy when testing AI-based systems:
"The management of personal data and sensitive data is often a concern during testing, as testing typically requires realistic data and it is difficult to fully anonymize data."
NEW QUESTION # 130
You are conducting a user acceptance test of a decision support recommendation system used in a data processing business. In addition to testing the functional performance of the recommendation systems, what else would you be MOST likely to test?
Answer: B
Explanation:
The correct answer is D . A decision support recommendation system does not merely produce predictions; it influences human decision-making. The CT-AI syllabus explains that automation bias, also called complacency bias, occurs when humans become too trusting of automated recommendations. One form occurs when a human accepts system recommendations and fails to consider other sources, including their own judgment.
For that reason, testing should cover both the quality of the system's recommendations and the quality of the corresponding human input provided by representative users. The syllabus explicitly states that testers should understand how human decision-making may be compromised and should test both the system recommendations and the human input.
Options A and C are still functional performance measures of the recommendation system. Option B is a performance efficiency concern and may be relevant at system testing, but it does not address the decision- support risk. Option D directly targets the critical acceptance concern: whether users can detect and appropriately handle inaccurate AI recommendations rather than blindly following them.
References/topics: CT-AI Syllabus Chapter 7, Section 7.4 "Testing for Automation Bias in AI-Based Systems"; Section 7.2.6 "Acceptance Testing."
NEW QUESTION # 131
Which statement regarding testing transparency, explainability, or interpretability is MOST correct?
Choose ONE option (1 out of 4)
Answer: D
Explanation:
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 isnot generally 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 are approximations, not exact reasons.
Therefore,Option Bis the most syllabus-aligned and correct statement.
NEW QUESTION # 132
Which of the following technologies for implementing AI is considered to be a reasoning technique?
Choose ONE option (1 out of 4)
Answer: C
Explanation:
TheISTQB Certified Tester AI Testing Syllabus v1.0explicitly categorizes different AI implementation technologies in Section1.4 - AI Technologies. Within this section, AI methods are grouped into categories, one of which is"Reasoning techniques."These reasoning techniques includerule engines, deductive classifiers, case-based reasoning, and procedural reasoning. Because deductive classifiers are directly listed under this set of reasoning approaches, they are recognized as a reasoning-based AI technology.
Reasoning techniques differ from machine learning approaches because they rely onstructured, predefined rules or logicto reach conclusions. Deductive classifiers use logical inference and symbolic reasoning to classify inputs by applying encoded knowledge. This makes them fundamentally different from statistical or data-driven ML algorithms.
The other options-Linear regression,Random Forest, andGenetic algorithms-are listed by the syllabus asmachine learning techniques, not reasoning methods. Linear regression performs numerical prediction, Random Forest is an ensemble decision-tree ML model, and genetic algorithms are optimization-based ML approaches inspired by evolutionary processes. None of these involve symbolic logical deduction.
Thus, based on the authoritative definitions in the syllabus,Deductive classifiers (Option A)is the only technology classified as a reasoning technique.
NEW QUESTION # 133
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