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| Certification Vendor: | ISTQB |
|---|---|
| Exam Name: | ISTQB Certified Tester - AI Testing Exam |
| Exam Number: | CT-AI |
| Exam Format: | Multiple-choice questions, 1-2 points per question |
| Available Languages: | Chinese, English, French, Korean, German, Spanish, Japanese, Portuguese |
| Related Certifications: | ISTQB Certified Tester Testing with Generative AI (CT-GenAI) ISTQB Certified Tester Foundation Level (CTFL) |
| Passing Score: | 65% (29/44 points for v2.0; 31/47 points for v1.0) |
| Exam Duration: | 60 (75 for non-native language) |
| Real Exam Qty: | 40 |
| Certificate Validity Period: | Valid indefinitely (no expiration) |
| Exam Price: | โฌ180 - โฌ250 (varies by region and provider) |
| Recommended Training: | CT-AI Syllabus v2.0 ISTQB Accredited Training Providers |
| Exam Registration: | iSQI Exam Registration Pearson VUE ISTQB Official Registration |
| Sample Questions: | ISTQB CT-AI Sample Questions |
| Exam Way: | Online remote proctored / Onsite test center |
| Pre Condition: | Must hold ISTQB Certified Tester Foundation Level (CTFL) certification |
| Official Syllabus URL: | https://istqb.org/certifications/certified-tester-ai-testing-ct-ai/ |
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NEW QUESTION # 32
Which ONE of the following statements is a CORRECT adversarial example in the context of machine learning systems that are working on image classifiers.
SELECT ONE OPTION
Answer: B
Explanation:
A . Black box attacks based on adversarial examples create an exact duplicate model of the original.
Black box attacks do not create an exact duplicate model. Instead, they exploit the model by querying it and using the outputs to craft adversarial examples without knowledge of the internal workings.
B . These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.
Adversarial examples typically cause the model to predict the incorrect class rather than just reducing accuracy. These examples are designed to be visually indistinguishable from the original image but lead to incorrect classifications.
C . These attacks can't be prevented by retraining the model with these examples augmented to the training data.
This statement is incorrect because retraining the model with adversarial examples included in the training data can help the model learn to resist such attacks, a technique known as adversarial training.
D . These examples are model specific and are not likely to cause another model trained on the same task to fail.
Adversarial examples are often model-specific, meaning that they exploit the specific weaknesses of a particular model. While some adversarial examples might transfer between models, many are tailored to the specific model they were generated for and may not affect other models trained on the same task.
Therefore, the correct answer is D because adversarial examples are typically model-specific and may not cause another model trained on the same task to fail.
NEW QUESTION # 33
Which statement describes factors related to test data that make testing AI-based systems difficult?
Choose ONE option (1 out of 4)
Answer: C
Explanation:
Section2.2 - Data Preparationand4.1 - Challenges in Testing AI-Based Systemsdescribe difficulties in obtaining and managing large, representative datasets. AI-based systems requirerealistic, diverse, and representativedata reflecting real-world variations. The syllabus emphasizes that assembling such datasets is time-consuming, resource-intensive, and often constrained by availability, privacy, or domain complexity.
Option B directly corresponds to these documented challenges.
Option A is incorrect: using the same implementation risksdefect masking, not preventing it; the syllabus warns against this practice. Option C is incorrect because real-world data naturally evolves, and the syllabus notes thatdriftis normal; expecting stable input data contradicts operational reality. Option D is incorrect:
although data privacy is important, the syllabus does not claim that artificially generated data always requires legal approval, nor that sanitization/encryption is mandatory for synthetic data.
Thus,Option Baccurately reflects syllabus-defined difficulties in producing representative test data.
NEW QUESTION # 34
Which ONE of the following options is the MOST APPROPRIATE stage of the ML workflow to set model and algorithm hyperparameters?
SELECT ONE OPTION
Answer: A
Explanation:
Setting model and algorithm hyperparameters is an essential step in the machine learning workflow, primarily occurring during the tuning phase.
* Evaluating the model (A): This stage involves assessing the model's performance using metrics and does not typically include the setting of hyperparameters.
* Deploying the model (B): Deployment is the stage where the model is put into production and used in real-world applications. Hyperparameters should already be set before this stage.
* Tuning the model (C): This is the correct stage where hyperparameters are set. Tuning involves adjusting the hyperparameters to optimize the model's performance.
* Data testing (D): Data testing involves ensuring the quality and integrity of the data used for training and testing the model. It does not include setting hyperparameters.
Hence, the most appropriate stage of the ML workflow to set model and algorithm hyperparameters isC.
Tuning the model.
:
ISTQB CT-AI Syllabus Section 3.2 on the ML Workflow outlines the different stages of the ML process, including the tuning phase where hyperparameters are set.
Sample Exam Questions document, Question #31 specifically addresses the stage in the ML workflow where hyperparameters are configured.
NEW QUESTION # 35
Which assignment of AI techniques to testing support is BEST?
Answer: C
Explanation:
The ISTQB CT-AI syllabus (Section5.2 - AI for Testing) explains that various AI approaches can support testing activities. Probabilistic methods--one of the three major AI technique groups--are used topredict system failures, especially when dealing with uncertainty, likelihood estimation, and reliability analysis. This aligns precisely with Option B.
NEW QUESTION # 36
A tourist calls an airline to book a ticket and is connected with an automated system which is able to recognize speech, understand requests related to purchasing a ticket, and provide relevant travel options. When the tourist asks about the expected weather at the destination or potential impacts on operations because of the tight labor market the only response from the automated system is: "I don't understand your question." This AI system should be categorized as?
Answer: D
Explanation:
Narrow AI (also known as Weak AI) is designed to perform specific tasks or solve particular problems within a limited domain, such as booking a ticket or recognizing speech. In this case, the AI system is specialized in handling flight bookings and related tasks but is not capable of addressing broader or more complex questions, like weather conditions or labor market impacts.
NEW QUESTION # 37
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P.S. Free 2026 ISTQB CT-AI dumps are available on Google Drive shared by DumpsFree: https://drive.google.com/open?id=144mQc1_2hkrYfOQdjae11WACsrp8HIxk