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| Certification Vendor: | ISTQB |
|---|---|
| Exam Name: | ISTQB Certified Tester - AI Testing |
| Exam Number: | CT-AI |
| Exam Duration: | 60 minutes |
| Exam Format: | Multiple Choice |
| Related Certifications: | ISTQB CTAL-TTA ISTQB CTFL |
| Exam Price: | EUR 250 |
| Available Languages: | English |
| Passing Score: | 65% |
| Certificate Validity Period: | Lifetime (no expiration) |
| Real Exam Qty: | 40 |
| 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 # 18
Which statement about testing levels for AI-based systems is correct?
Answer: B
Explanation:
Section4.3 - Test Levels for AI Systemsclearly defines ML model testing as the level at which testers evaluate whether an ML model fulfills itsfunctional performance criteria, including accuracy, precision, recall, F1, robustness, stability, and fairness. Therefore, Option C is the correct and syllabus-aligned statement.
NEW QUESTION # 19
Which ONE of the below statements BEST describes how combinatorial testing can be applied to AI-based systems?
Answer: C
Explanation:
Combinatorial testing involves testing different combinations of input parameters to ensure coverage of various interactions. In the case of AI-based systems, inputs to the system (such as data features) and environment factors (like conditions under which the system operates) can be considered parameters for pairwise tests. This ensures that different combinations of inputs and environmental factors are tested for their effect on the system's behavior.
NEW QUESTION # 20
A word processing company is developing an automatic text correction tool. A machine learning algorithm was used to develop the auto text correction feature. The testers have discovered when they start typing "Isle of Wight" it fills in "Isle of Eight". Several UAT testers have accepted this change without noticing. What type of bias is this?
Answer: D
Explanation:
Automation bias, also known as complacency bias, occurs when humans over-rely on automated systems and fail to question or validate the system's output. In this scenario, the auto-text correction feature of the word processing tool incorrectly suggests "Isle of Eight" instead of "Isle of Wight." The issue arises because multiple UAT testers accept the incorrect suggestion without noticing it, demonstrating a reliance on the AI- based system rather than their own judgment.
Automation bias is commonly seen in:
* Text correction systems, where users accept incorrect suggestions without verifying them.
* Medical diagnosis AI tools, where doctors may rely too much on AI recommendations.
* Autonomous driving systems, where drivers become overly dependent on automation and fail to react in critical situations.
* Section 7.4 - Testing for Automation Bias in AI-Based Systemsexplains that automation bias occurs when people accept AI-generated outputs without verifying them, often leading to incorrect decisions.
Reference from ISTQB Certified Tester AI Testing Study Guide:
NEW QUESTION # 21
A test engineer is planning the best functional performance metrics to evaluate an unsupervised learning model. The model groups data points based on their similarity. The test engineer wants to measure how similar the data points in each group actually are. Which is the MOST likely metric they should use:
Answer: C
Explanation:
The correct answer is B. Intra cluster . The scenario describes an unsupervised clustering model, where the objective is to group data points based on similarity. The requested measurement is specifically how similar the data points are within each group , which maps directly to intra-cluster metrics . The CT-AI syllabus states that inter-cluster metrics, intra-cluster metrics, and the silhouette coefficient may be used for unsupervised clustering problems.
Option A, ROC, and option C, AUC, are used for supervised classification, especially binary classifiers, to evaluate how well the classifier distinguishes between classes. Option D, recall, is also a supervised classification metric and measures how many actual positives were correctly predicted. None of those metrics is designed to evaluate compactness or similarity within clusters. Intra-cluster measurement is the most direct metric family because it evaluates the cohesion of a cluster: the closer or more similar the members are, the stronger the clustering result is likely to be.
References/topics: CT-AI Syllabus Chapter 5, Section 5.2 "Additional ML Functional Performance Metrics for Classification, Regression and Clustering"; Section 5.4 "Selecting ML Functional Performance Metrics."
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NEW QUESTION # 22
Which characteristic of AI-based systems makes it difficult to ensure they are safe (e.g., not harming humans)?
Choose ONE option (1 out of 4)
Answer: C
Explanation:
The ISTQB CT-AI syllabus lists several characteristics that make it difficult to ensure safety in AI-based systems. Section2.8 - Safety and AIexplicitly names the characteristics that complicate safety assurance:
complexity,non-determinism,probabilistic behavior,self-learning,lack of transparency, andlack of robustness.
Among these,complexityis a core challenge because modern AI systems-particularly those using deep learning-have highly non-linear behavior, large numbers of parameters, and intricate interactions that are hard to predict.
Option B (Complexity) directly aligns with the syllabus and is therefore correct.
Option A (Determinism) is the opposite of AI behavior; AI is oftennon-deterministic, and determinism doesnotmake systems unsafe. Option C (Interpretability) does impact trust and explainability, but the syllabus positions it as a transparency challenge, not the primary difficulty in ensuring safety. Option D (Robustness) is a desired quality, not a reason safety is hard; alackof robustness would be a challenge, not robustness itself.
Thus,complexitybest reflects the syllabus' explicit safety-related difficulty.
NEW QUESTION # 23
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